Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

The global pandemic has sparked the popularity of robots in facility management tasks (FMTs) such as floor cleaning and disinfection. This trend brings many task scenarios where humans and robots need to cooperate with each other. Effective human–robot collaboration (HRC) relies on precise communication of the robots’ intentions (e.g., to move to a position, or to grasp an object), so that their human counterparts can adapt their behaviors/actions accordingly. However, little has been known on how this can be done in FMTs. Visualization technologies have potential to enhance HRC by communicating the robot intention in a visualized manner. This research aims to develop a framework that integrates the latest visualization technologies, e.g., building information modelling (BIM) and augmented reality (AR), to enhance HRC in facility management. The framework includes two complementary modules: (a) a remote monitoring module (RMM) that can remotely transmit and visualize robot information in a Web-based BIM to inform decision-making, and (b) an onsite collaboration module (OCM) that augments human co-workers with real-time robot intention to allow effective cooperation. Experiments were conducted to validate the proposed framework in typical FMTs. Results show that the integrated visualization framework can intuitively and unambiguously convey robots’ intentions to their human counterparts, significantly improving the performance of HRC. Future research is suggested to complement the framework with a reverse mechanism to effectively convey human intentions to robots.

Automation and robot have been strong roles in improving productivity and safety of the construction industry. While a quantity of research and practice are conducted on new-type construction robots, there is a lack of robotization for traditional construction equipment. This research presents a robotic truck crane system (RTCS), which provides traditional truck cranes with automatic operation abilities. It is a system consisting of real hardware and modular-designed software, providing monitoring, path planning and automatic execution functions. An experiment was carried out on a scaled prototype to verify the feasibility and efficiency of the system. The result shows satisfactory operational accuracy, path planning capability, and accessibility of the system.

Open spaces such as parks and gardens provide a variety of ecosystem services that enhance human physical and mental well-being. Previous studies have extensively investigated the homebuyers’ willingness to pay for the utility of open space. However, few studies have investigated the price elasticity of open space to exogenous shock such as the COVID-19 pandemic. Due to massive social distancing and travel restrictions, open space within walkable distance is hypothesized to be appreciated under the pandemic, especially in high density cities like Hong Kong. Does this shock a one-off incident or create a lasting effect on price gradient? This article addresses this question by employing a multi-level difference-in-difference (DID) model based on property transaction data from 2019 to 2021 in the Hong Kong housing market, aiming to unravel the dynamic relationship between open space and residential property price during the COVID-19 pandemic. The results show that: (1) the price gap between proximate and distant properties from open space was widened, steepening the price gradient; (2) the premium effect of open space was time-varying and mainly appreciated in the later stage of the pandemic; (3) low-to-medium-wealth communities showed higher elasticity of open space to the COVID-19 pandemic than high-wealth communities; (4) the utility of open space is more appreciated in highly-infected communities. This article contributes to the juxtaposition of price elasticity, exogenous shock, and urban environments. The concerns of open space inequity and housing affordability are also raised, which calls for the policy coordination for housing, public health, and urban planning.

The application of digital twin technology (DT) in construction is becoming more and more widespread, and within this, construction safety management has always been an important part that cannot be ignored. The development of digital twin technology provides new possibilities on how to improve construction safety management using DT. This paper develops a conceptual human-machine-environment safety detection and control system based on identifying potential applications of digital twin technology focuses on unsafe behavior, unsafe machine/equipment condition and unsafe environment as the main subjects. This system includes a five-dimensional model of the digital twin technology, including cyber-physical modeling, data storage and management, perspective simulation, deep learning, unusual state diagnosis and trend prediction. In addition, key technologies, such as data mining and simulation, and twin collaboration for human-machine-environment co-integration, have also been designed. In addition, the case of Qinling Tunnel digital twin technology application is studied. The research provides several threads for the integration of digital twin technology and construction safety and several areas for future research. The research makes contribution to the field by applying emerging information and communication technology-based management of construction safety.

Interview holds a prominent place among qualitative research methods, and it has been widely used in construction and demolition waste management research. Based on the literature review of interview-based related research, the status quo of interview applications is analyzed. Consequently, an interview guideline of interview-related methods is proposed according to the ratio between the number of interviewers and interviewees for effective implementation of interview methods. This paper classifies interview methods into four scenarios: Type I (one to one, 1:1), Type II (one to many, 1: n), Type III (many to one, m:1), and Type IV (many to many, m:n). Through a series of case studies, the application of the guide is demonstrated, and the corresponding best practices are summarized. Overall, this paper provides references for researchers to select the most appropriate interview method in construction and demolition waste research, and manage the interview process effectively.

With the progress of the society and technology, there are more and more high-rise buildings, while followed by high-rise building fire problem has become a worldwide problem. Through stating the number of high-rise building fires, the elaboration of high-rise fire risk and its characteristics, analyse the urgency and difficulties faced by the high-rise building fire rescue. Moreover, make the comparative analysis of data information differences between GIS and BIM. Finally, based on the application of the fire command system of GIS, the fire command flow chart of BIM-GIS applied in building is put forward, which can help improve the high-rise building fire and rescue command management.

There is a rapidly spread sand and gravel supply shortage to meet increasing construction material needs. A considerable quantity of sand and gravel are stored in the built environment and have the value of being a secondary material resources. Using the bottom-up material flow analysis (MFA), this study estimates the flow and stock of sand and gravel in Shenzhen buildings and evaluates associated environmental impact from the material production stage. The results show that the consumption of sand and gravel in Shenzhen buildings exceeded 439 million tons (Mt) from 1979 to 2019, with an average annual consumption of 10.9 Mt. Equivalently, 598 kilotons (Kt) of the embodied carbon emissions were generated in the production stage of the sand and gravel. The cumulative outflow (measured by the generation of demolition waste) of sand and gravel exceeded 150Mt, accounting for 34% of the total consumption. Moreover, the sand and gravel stocks in Shenzhen buildings have boomed to 302 Mt in 2019 (23 t/cap), and show characteristics of fast inflow and slow outflow. This study helps understand the dynamic metabolism of sand and gravel in buildings and forms the data basis for further research in urban sand and gravel resources, waste management, and environmental strategy.

Virtual labs based on serious games have shown their potential to support experiment teaching, particularly for online engineering education during and after the global COVID-19 pandemic. However, how to develop serious games to support experiment teaching is still unclear. This article applied the Unity game engine to develop a virtual lab for the leveling experiment to show the process of virtual lab development. Ten students majoring in construction were recruited to test the developed virtual lab. After the experiment, participants completed a questionnaire including six questions about their feedback on the developed virtual lab. It can be concluded that the develop a virtual lab that allows students to actively join in and experience the whole experiment process better help students master related knowledge and skills. The developed virtual labs can therefore well aid in future experiment teaching of engineering surveying for construction majors. More efforts are needed to enrich the range of experiments and improve the user experience to promote virtual labs in engineering education.

The road repair and maintenance process will produce a large amount of reclaimed asphalt pavement (RAP). The abundant generation of construction waste poses a major challenge to the sustainable development of resources worldwide. Based on the increasingly prominent contradiction between environmental resources and construction development, the implementation of resource utilization of recycled asphalt pavement materials is of great significance for resource sustainability. This paper aims to study the factors influencing the behavior of resource utilization of RAP materials from different stakeholders’ perspectives. Based on the theory of planned behavior (TPB), three variables of “government supervision”, “potential benefits” and “project constraints” are introduced to meet the specific situation of road projects, and the model of RAP resource unitization behavior is constructed. Data were collected through questionnaires, and structural equation modeling (SEM) was used to test the theoretical model and research hypotheses. The influencing factors that cause the difference of RAP resource utilization behavior among stakeholders were analyzed through multi-group structural equation. The research findings show that attitude towards behavior, subjective norms and perceived behavioral control have a significant positive impact on RAP resource utilization behavioral intentions. Perceived behavioral control, project constraints and behavioral intention have a significant positive impact on RAP resource utilization behavior. The hypothetical path of potential benefits has a non-significant effect on actual RAP resource utilization behavior. The multi-group structural equation analysis with stakeholders as moderating variables showed that the effects of government supervision, project constraints, and behavioral intentions on actual behavior were more significant for the designer; the effects of attitude towards behavior on behavioral intentions of RAP resource utilization were more significant for the contractor; and the effects of perceived behavioral control variables on behavioral intentions and actual behavior of RAP resource utilization were more significant for the owner. The research is expected to provide a theoretical basis to guide the stakeholders better implementing RAP resource utilization measures.

The E-inspection platform provides a non-physical onsite presence solution for inspectors. The supervision units can inspect the tasks by online submitted documents. The image is an essential data source on the E-inspection platform because it can provide rich and intuitive information for inspectors. However, if the quality of the submitted image is low, the uploader needs to re-upload the record for inspection, which reduces the efficiency. The image quality assessment (IQA) metric can assess image quality quantitatively. The application of the IQA metric in the E-inspection platform can help the inspection record uploader recognize the low-quality image before submission. In this research, different IQA metrics are selected and conducted in an actual E-inspection task to illustrate efficiency and effectiveness.

The uncoordinated contradiction between housing supply and demand is serious. To address this issue, it is crucial to focus on the perspective of urban residents and promote the matching between housing and residents through housing choice. Relying on the theory of consumer behavior and “Stimulus-Organism-Response” paradigm, the three dimensions of housing choice motivations were refined as rent-purchase choice, space choice, and time choice. And the two levels of housing consumption perception were refined as perceived value and perceived risk. Then, the interactive relationship between the three dimensions of housing choice motivation, as well as the impact path of pairwise interaction items on housing consumption perception and housing choice willingness was revealed. Through economic experiments, this study has investigated the housing choice willingness of residents in Harbin. The results show a pairwise interaction among rent-purchase choice, space choice, and time choice. All pairwise interactions significantly affect perceived risk. The pairwise interaction between rent-purchase choice and space choice has a significant impact on both perceived value and perceived risk. Perceived value positively affects residents’ willingness to choose housing, while the impact of perceived risk on housing choice willingness is negative. It will help residents to choose housing rationally, and provide implications for the government to formulate housing policies and guide the behavior of housing market participants.

Energy consumptions due to buildings account for around 1/3 of the global energy consumptions, which addresses the importance of reducing energy uses in buildings. In the age of big data, the digitalization process is helping the energy savings in the building industry. However, semantic interoperability between data in multiple systems/software is lacking, which hinders green building design and operation management. Semantic Web technologies (SWT), connecting machine-readable concepts that characterize real-world objects, are beneficial for representing and reasoning the data in building energy savings. This study aimed at reviewing the related works from 2011 to 2022 which were relevant to the applications of SWT in the reduction of building energy. Reviewed studies were categorized into 3 groups which were reducing energy loads during the design processes, applying renewable energy in buildings, and energy-efficient building systems. The applications of SWT utilizations of energy savings in building designs and system operations were reviewed. SWT is found beneficial to energy saving because it helps organize resources, supports the decision-making processes, improves design and management efficiency, facilitates querying and interoperability, and assists big data analysis. With the assistance of SWT, building energy could be saved by 2.11% to 40% according to the results of the literature. The SWT-based studies in the field of reducing building energy are still in their infancy. Further studies might focus on more SWT utilizations of building lifecycle for energy reductions, extending ontologies to more building systems and types, more complex HVAC control and FDD, and more energy analysis tools.

Nowadays, waste management is more and more important due to the increasing attention paid to the environment and human sustainability. To examine construction and demolition waste (CDW) management factors, this study adopts the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach and analyzes data collected from 8 experts. Among the 11 key factors identified from existing research, Government subsidies and other supports and Waste-related trading system of CDW are the most important factors to promote waste management. Interestingly, Investment of the related enterprises, which was taken as an important factor, is identified as a passive key factor in this study. This study provides new evidence to improve the management practice of CDW and can help enhance the related management system. Limitations and future research directions are also introduced.

Automatic Construction Robot (ACR) replacement is increasingly important in addressing labor shortages, productivity inefficiencies, and other problems in construction activities. However, the development of ACR replacement cannot be separated from technology forecasting. Given patent data as an essential indicator for predicting technology development, this study aims to develop a technology prediction model to analyze the development of ACR replacement. To this end, this study first collected patent data on the ACR. Secondly, this study proposed a technology mining method and patent similarity model based on the subject-action-object (SAO) structure to build the patent similarity matrix. Finally, this similarity matrix was used to draw the patent map for forecasting the development direction of ACR replacement. This study identified the eight development opportunities of ACR replacement and related challenges.

Affected by the “COVID-19” epidemic, the safety of urban public health has been challenged, and improving the adaptability of community health has become an important link to realize the “Healthy China” strategy. Therefore, this paper analyzes the influencing factors of health adaptability of community transformation in the post-epidemic era, in order to provide theoretical references for improving the construction of healthy communities. Through in-depth analysis of the concept of healthy community and literature investigation, this study established a research model based on four conditional variables (spatial health, environmental health, service health, and health performance optimization) and the outcome variable (community health adaptability), and analyzed 416 questionnaires of community transformation by fuzzy set qualitative comparative analysis (fsQCA). The results show that spatial health and health performance optimization are the core conditions that affect the adaptability of community health after renovation. Through the analysis of the combination of each condition, this paper establishes the spatial path and the optimization path, the combination of which has an impact on the community health transformation.

With the modernization, transformation and upgrading of China’s construction industry, the transformation of migrant workers into industrial workers in the construction industry is also an inevitable trend. Therefore, it is particularly important to explore the factors affecting the transformation of migrant workers. First of all, the paper identified 26 factors affecting the transformation of migrant workers in the construction industry into industrial workers through literature research and expert interviews. Secondly, through spss26.0 statistical analysis software, the data recovered from questionnaires are analyzed, key factors are extracted, and five categories of major influencing factors are identified. According to the importance the rankings are: personal and enterprise factors, institutional factors, policy factors, industry factors and market factors. Finally, countermeasures and suggestions are put forward to provide reference for accelerating the industrialization of China’s construction industry and migrant workers.

Safety compliance is crucial in maintaining construction safety. This study applied the concept of differentiating safety compliance into deep safety compliance and surface safety compliance and set out to investigate the effects of perceived ease of use, management commitment to safety, and safety communication on deep and surface safety compliance, respectively. By using partial least squares structural equation modeling (PLS-SEM), a model was developed and validated based on the cross-sectional survey data collected from 239 construction workers in Australia. Results revealed that safety communication could directly and positively impact deep safety compliance, while negatively impact surface safety compliance. Moreover, management commitment to safety was found to impact safety communication directly and positively, while perceived ease of use could moderate this relationship. Last but not least, safety communication played a mediating role in the relationship between management commitment to safety, and deep and surface safety compliance. By investigating the contributing factors of deep and surface safety compliance in the construction industry, this study contributes to the literature on safety management and provides practical implications as understanding the proposed model helps site managers or safety professionals enhance safety performance by achieving deep safety compliance or minimizing surface safety compliance.

In order to improve the sustainability of urban construction and development, governments at all levels in China have launched a large number of policies related to urban green planning one after another. However, the program design of urban green planning itself is a complex system engineering, and the scientificity of program decision is important for the smooth implementation and sustainability effect of urban planning programs. To this end, this paper proposes a GRA-Fuzzy-based urban green planning scheme evaluation model. The method starts from the program itself, analyzes the operation and guarantee measures of the program, and combines with the analysis of relevant literature to establish the evaluation index system of urban green planning program; on the basis of fully considering the uncertainty, coherence, fuzziness, and grayness of the evaluation index of urban green planning program, it combines the non-addressable Shapley value assignment method, fuzzy and gray system theory to form a comprehensive evaluation model of urban green planning. Finally, the empirical study shows that the model can effectively evaluate the urban green planning scheme.

Carbon lock-in is becoming increasingly prominent in China’s industrialization. Understanding regional carbon lock-in and finding the path of carbon unlocking are of great significance for China to achieve carbon peak and carbon neutralization. Based on the qualitative comparative perspective, this paper systematically defined the five stages of regional carbon lock-in (carbon attraction, carbon clustering, carbon lock-in, unlocking transition, and relative unlocking) at the provincial level between 2000 and 2019 and analyzed their historical evolution characteristics. Further, the forming paths of carbon lock-in and unlocking were explored by using fuzzy-set qualitative comparative analysis (fsQCA). The results showed that during the past 20 years, the general trend of carbon lock-in in China was from the gradual formation of carbon lock-in to carbon unlocking. In addition, there were nine paths of carbon lock-in and five paths of unlocking. The carbon lock-in paths can be summarized as Government Driven, Technological Lag, Development Backward, and Extensive Development, and the unlocking paths can be classified as Social Unlocking and Economy Driven. Using a quantitative method to describe the qualitative definition of carbon lock-in and unlocking, this study provided a systematic perspective on the transition of carbon lock-in in China and offered unlocking recommendations for carbon-locked regions.

The investment in real estate is an essential part of the regional economic development. In the digital economy, the data element has promoted economic development by accelerating information dissemination. Despite prior studies that have found that real estate investment can positively affect economic development, limited empirical studies explore this relationship based on new production elements perspective. In this study, the data samples are obtained from 31 provinces of China from 2006 to 2019. The OLS regression model is established to evaluate the influence of the investment in real estate on economic development and further examine the change of this kind of influence from regional heterogeneity and temporal heterogeneity aspects. This study demonstrates that the investment in real estate can influence the economy positively when the data element is involved in production. The influence of investment in real estate on economic growth in developed regions is lower than that of undeveloped regions. The influence was positive and significant before 2010, while the influence of the investment in real estate on economic development was negative after 2010. Besides, the data element has gradually become essential in driving economic growth. The government should control the investment in real estate and emphasize the value of data element to facilitate the real estate industry.

Many studies show that individual unsafe behavior is the leading cause of construction safety accidents, of which 94% of accident-induced causes are related to unsafe human psychology. Psychological capital, as a unique psychological resource of individuals, plays an important role in motivating individuals to implement safe behaviors. Therefore, it is essential to explore the influence of construction workers’ psychological capital on unsafe behaviors, which helps to control their unsafe behaviors and provides strong guidance for construction enterprises. In addition, the psychological capital of construction workers is one of the important endogenous factors affecting unsafe behavior. This research explores the relationship between construction workers’ psychological capital and unsafe behaviors. It carried out surveys of construction workers in Shanghai by questionnaires. The psychological capital, safety attitudes, and unsafe behaviors of construction workers were used respectively as exogenous, meditating, and endogenous variables to construct the structural equation model. The following conclusions were obtained: first, the three dimensions of self-efficacy, hope, and resilience significantly affect unsafe behavior, and the relationship between optimism and unsafe behavior is not significant; second, self-efficacy, optimism, hope, and resilience have significant effects on safety attitudes; third, self-efficacy, optimism, hope, and resilience indirectly affect construction workers’ safety attitudes by influencing construction workers’ unsafe behaviors; finally, we propose management countermeasures to improve the workers’ psychological capital and safety attitude based on the research results.

With the increasing demand for healthcare in China, the retrofit of old hospitals has become an effective practice to upgrade hospitals and improve the healthcare environment. How to fully utilise digital design to retrofit old buildings to improve functionality, reduce energy consumption, shorten building cycles and reduce retrofit costs becomes a significant challenge for the industry to meet the environmental quality needs of patients and healthcare workers. This research explores the industry practice of a leading hospital design institute in China by adopting a single case study of the Beijing Ditan Hospital retrofit project. It reveals the status quo, challenges and prospects of adopting BIM-enabled design in China’s hospital retrofit. The results show that the BIM-enabled design faced the challenges of the Beijing Ditan Hospital retrofit in terms of the complexity of design requirements, functional partitioning and environmental changes through six functions: storage, integration, connection, automation, visualisation and statistical analysis.

Construction 4.0 (C4.0) was begotten with emergent and disruptive technologies in the construction industry. Despite the promising benefits such as productivity and competitiveness, scholars and practitioners are still struggling with how to promote C4.0 technology implementation (C4.0TechIm) in construction. Previous discourses primarily lay in certain aspects of C4.0 without a holistic view of the influencing factors of C4.0TechIm. This study thus seeks to unveil the influencing factors of C4.0 technology implementation based on the twin sustainable and digital transition perspectives and guide future research needs. The authors reviewed the 77 relevant articles, identified a comprehensive list of 60 influencing factors, and used simplified analysis to quantify the factors. It is found that knowledge gaps exist in developing decision-making models that cover all the identified factors in an integrated framework. Practically, the results provide references to accelerate C4.0 technology implementation and offer strategies for maximizing potential benefits.

In recent years, construction and demolition (C&D) waste issues have become increasingly prominent, especially in developed urban areas, where a huge amount of waste generated can not be well recycled. Such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), where 11 cities are concentrated. To gain a deeper understanding of the current situation of C&D waste management in GBA is critical for developing “zero waste” cities. The study aims to explore the current situation of C&D waste management in GBA from the regional level via desktop survey. The results include: (1) The generation amount and treatment capacity of C&D waste in GBA; (2) The C&D waste treatment and disposal methods and related facilities in GBA; (3) The promulgation of policies and regulations related to C&D waste management in GBA. The paper is valuable as it provides fundamental data for subsequent research on C&D waste in GBA and references for other regions to improve waste management performance.

Resilience is a flourishing topic in recent years, whose key is to help cities to absorb, recover and adapt after being disturbed. Cities are the main carriers to support human activities, whose resilience appealed numerous experts’ attention, both in theoretical and practical. Therefore, this paper mainly conducted a systematic review on urban resilience, aiming to provide some experiences and understanding in studying urban resilience, and to contribute the development of the area of urban resilience. Specifically, this study starts from the evolution of the concept of “resilience”, which is experienced from engineering resilience to ecological resilience, then developed into socio-economic resilience. According to this, a comparative analysis between these three concepts was conducted and the distinction among them is obtained. Followed by that, a literature review of the definition and framework of urban resilience is conducted. In line with this, an interesting finding is excavated, namely, previous frameworks mainly consider more “urban” and put less attention to “resilience”. However, “dynamic” should be the essence of resilience, and it is necessary in evaluating it. Therefore, this study takes this characteristic that “resilience” embraced, namely “dynamic”, to redefine urban resilience. Finally, concerns to the framework of urban resilience are also given. In this step, mainly two kinds of frameworks are collected, namely framework for urban resilience and community resilience. The reason is that community is commonly recognized as the minuscule unit of a city, whose resilience plays a basic for the urban resilience, it is therefore the literatures about it should be included. Concluded, this paper argues that urban resilience is a new way to guide the sustainable development of modern cities. An accurate understanding of the conceptual connotation, content framework and characteristic standard of urban resilience have important guiding value for enhancing the adaptability of modern cities.

In this paper, we analyze cement price fluctuations and predict cement price. Taking the daily price data of ordinary silicate 42.5R (bulk) cement in Guangzhou from July 2013 to April 2022 as an example, we used Ensemble Empirical Mode Decomposition method to process cement price time series, then obtained from high to low frequency three parts of the intrinsic mode function (IMF) and the residuals (RES), from the perspective of influencing factors, explained the price fluctuation of cement. In the context of EEMD decomposition results, we made hierarchical forecasts and integrated final price value results, ARIMA model and RBF neural network prediction method are compared which can predict the cement price value more accurately. The result shows the sequence decomposed by EEMD has many advantages in simulating cement price prediction, based on the evaluation indicators of the prediction results such as MAE, RMSE and R2, it is determined that is a better combination method for predicting cement price fluctuations. All findings indicate that this paper provides a suitable approach for predicting the price fluctuation of civil engineering materials, and propose suggestions for project managers to deal with the risk of price fluctuations, try to prevent hidden cost problems caused by the cost deviation of engineering materials.

Accidents due to the use of construction equipment are one of the leading causes of serious injuries in the construction industry. The supervision of construction equipment is an essential procedure to ensure the safety of users in the usage stage by requiring appropriate and timely maintenance. However, the following two issues must be tackled to secure its effeteness. Firstly, the execution of maintenance must be guaranteed, especially under a tight construction schedule. Secondly, the immutability, transparency, and traceability of the maintenance information must be enhanced. This research aims to develop an i-Core and blockchain-enabled framework for assuring the effectiveness of supervision of construction equipment maintenance (ESCEM). First, a literature review was conducted to identify ESCEM challenges and i-Core and blockchain potentials. Then, an i-Core and blockchain-enabled conceptual framework was provided. Finally, a prototype system was developed based on a case study to instantiate and test the proposed framework. As a result, it is found that i-Core can protect the effectiveness of ESCEM by automatically notifying construction stakeholders of when equipment maintenance is due, ensuring timely maintenance. Besides, blockchain allows construction stakeholders to submit, share, endorse, and store the maintenance transactions of construction equipment in a decentralized, transparent, immutable, and traceable manner. This study offers a novel solution for construction equipment safety management to construction researchers and practitioners, inspiring more discussions about blockchain applications in the construction industry.

China actively responds to climate change and promotes green and low-carbon economic development. The energy-saving service companies (ESCOs) are key forces in energy-saving emission reduction, which provides services such as energy-consumption diagnosis, design, financing, transformation, and operation management of energy-saving projects, especially construction projects. This study adopts a data envelopment analysis (DEA) model to calculate the optimal scale of the number of employees and investment of ESCOs that effectively influences the energy-saving efficiency of energy-saving service industry. The study found that the optimal scale of employees and investment of ESCOs under the average production level is 140 people and $3.6 million by comparing the super-efficiency value of the top three years (i.e., the years 2006, 2020, and 2007). Some policy suggestions are proposed to develop the energy-saving service industry.

China, as one of the world's largest producers of municipal solid waste, is faced with a huge amount of waste problem. Unregulated landfills not only lead to environmental hazards, but also may have landslide risks. Remote sensing technology has the characteristics of long-distance, non-contact and periodicity, which can make up for the shortage of crrent manual management methods. Therefore, this study will use deep learning combined with remote sensing images to achieve automatic classification of landfill images. The study uses CB04 and sentinel2A satellite images to construct a landfill remote sensing image dataset, and divides the dataset into training set, validation set and test set according to the ratio of 6:2:2. In this study, two classical models, Vgg and ResNet, are used to implement image classification on landfill remote sensing image datasets. The results show that (1) in this study, a method for automatic classification of remote sensing images of landfills is proposed, with a model accuracy of 86.76%, and (2) deep learning can effectively implement automatic landfill classification, and both Resnet and Vgg have shown good classification results., and (3) Resnet and Vgg can better perform the task of automatic landfill identification after introducing the optimization strategy of parameter migration. As shown above, deep learning has strong potential in the field of landfill image classification.

The outbreak of SARS-CoV-2 (COVID-19) in high-density cities lead to the deficiency of healthcare facilities to receive patients. Emergent Modular Construction (EMC) has manifested high efficiency in project construction, considering cost, sustainability, quality control, and schedule management. EMC has been applied in natural catastrophes, or other emergencies, it was given a special name “Fangcang Shelter Hospital” (FSH) during the Covid-19 pandemic, which was utilized as a quarantine camp to isolate patients. However, rare attention has been paid to the management of FSH after rehabilitation or in the post-Covid-19 period, resulting in potential resource waste.An RFID-BIM-enabled framework was proposed to maximize the resource sustainability of construction in the post-calamity period. This method integrates two technologies, Building Information Modeling (BIM), which is a data-rich model to coordinate the production of a building's various systems, and automatic Radio Frequency Identification (RFID), which facilitates the process of detecting, identifying, sorting, and storing the information of the components of the modular unit, such as doors, walls, and steel frames, allowing the reuse of the building elements.

Unsafe behavior of construction workers is the major contributor to the prominent construction accidents, and more and more studies claimed that suffering from occupational stress could exacerbate the unsafe behavior of workers. Frontline professionals have the intensive interactions with workers and are responsible for managing and supporting workers to complete various construction tasks. Frontline professionals may adopt diverse leadership style, while it is likely that varying leadership style could lead to different outcomes in terms of stress and safety. This study aims to examine the influence of several leadership styles adopted by frontline professionals on the stress and safety behavior of construction workers through a two-steps study using mixed method. Firstly, a questionnaire survey was administered among construction workers to collect over 120 valid empirical data that were subjected to a series of statistical analyses, including exploratory factor analysis, reliability test and multiple regression models. Secondly, agent-based simulations were performed to verify the statistical results regarding the relationships between leadership style, stress and safety. The results show that: 1) emotional stress and burnout of construction workers could cause safety incompliance; 2) autocratic and destructive leadership styles consistently spur emotional stress and burnout of construction workers; and 3) it was interesting to find that safety-specific transformational leadership does directly promote safety participation of workers but not affect stress. The implications of the findings were discussed and practical recommendations were also made to enhance stress management and prevent unsafe behavior of construction workers. Through innovative application of mixed methods, this paper mainly contributed to answer how does varying leadership style lead to different safety outcomes from stress management perspective.

The building and construction industry has been recognized to be stagnant and slow in the digital transformation. Artificial Intelligence (AI) is at the forefront of transformative information technology, and it offers competitive advantages to the construction industry to be more resilient and sustainable. Despite of this, there is a knowledge gap to identify how contemporary AI practice supports the decision making for creating sustainable buildings and cities. This paper aims to explore the main areas of AI applications in the built environment for delivering the goals of sustainable buildings and cities. A survey design method was used to investigate AI applications in the contemporary building practice. Surveys were distributed to 100 respondents working in AI technology or service providers by emails and online platforms. AI systems are adopted in the areas of HVAC, water management, smart parking systems, and security and alarm system. Smart vehicle parking system is identified as the main area with wider AI applications, followed by security and alarm system, water management system and HVAC systems. Smart water management systems are identified as a key opportunity area for AI adoptions in buildings and cities. AI enabled systems can improve the efficiency of resources management and optimize the energy efficiency in the built environment. The adoption of AI can assist governance of sustainable building and cities by analysing data and information of a building’s complexity and a city’s dynamics. Apart from the digital transformation, AI also helps to achieve significant contributions in delivering the goals of sustainable buildings and cities.

Promoting urban resilience development in China is of great demonstration significance for China’s exploration of a new development pattern. An urban resilience measurement model including 24 indicators were constructed from five dimensions which are economy, culture, infrastructure, society and ecology. Resilience development level from 2011 to 2020 of 41 cities in China region were calculated according to the measurement model. A coupling coordination degree model was applied to analyze temporal and spatial evolution of the coupling coordination relationship between five dimensions. The coupling coordination degree of urban resilience had been rising in terms of temporal latitude, and was obviously different between cities in terms of spatial latitude. The results showed that coupling coordination degree of urban resilience in eastern Yangtze River Delta was higher than western Yangtze River Delta. Also it had a high coupling coordination degree of urban resilience in central Yangtze River Delta, which was much higher than north-south edge area.

Sewers are essential infrastructure for transporting urban wastewater, but their operational efficiency can be compromised by sedimentation. Unfortunately, CCTV (closed-circuit television) and advanced sewer sediment inspection technologies fail to quantify the volume of sediment and accurately locate the sediment distribution. Furthermore, most of the existing technologies are ineffective in a fully loaded sewer. This research presents a multisensor data fusion method for sewer sediment inspection by an autonomous underwater vehicle (AUV). An AUV is equipped with a rotating sonar device, a gyroscope, an accelerometer, and an odometer. Multisensor data fusion is divided into two steps. In the first step, the Kalman filtering technique is applied to fuse gyroscope, accelerometer, and odometer data to solve the problem of AUV localization in sewers with weak or no signal. In the second step, the point cloud data collected by sonar are fused with the fused sensor data to solve the point cloud data offset problem caused by the current and AUV motion. Overall, the proposed method has great practical potential for accurate sediment inspection in each section of a sewer, which can be used to support decision-making in urban sewer maintenance.

With the rapid development of the construction industry, there is a great requirement to cultivate a large number of management talents with higher competency (ability, quality and knowledge). Taking postgraduates in construction management as an example, this research reviewed the relevant literature on the three dimensions of ability, quality and knowledge which postgraduates in civil engineering and construction-related disciplines should have. The results show that in order to promote their competency, the most important abilities which postgraduates in construction management should cultivate are communication skill, practical working capability and leadership; the most important qualities which should be trained are professional quality, ideological and moral quality, physical and mental quality, and personality characteristics; and the most important knowledge which should be equipped is interdisciplinary knowledge, disciplinary professional knowledge and engineering practice knowledge. The results of this study can be used as the basis for relevant universities to formulate postgraduate programme, to further promote the competency of construction management postgraduates in China.

Most tower cranes require a very complex operating system in order to move objects accurately and safely. However, complex operations may distract the operator, which can lead to harmful accidents. Although existing blind spot monitoring systems have been successfully embedded in cars, simply transferring BSM to construction devices is impractical. In the dynamic construction environment, vehicles and workers work in the same area simultaneously, but the traditional assistant system has a high latency and is unable to provide real-time safety monitoring and alarms. To relieve this problem, this paper designs a YOLO fast-blind spot monitoring system. A YOLO-based system can monitor the tower crane’s blind spot from the bottom of the hook to assist in blind lifting and alert the operator when a potential object is present. This approach relies on edge computing devices to monitor objects’ behavior in an operating blind spot. The results show that this system can detect objects and alert the operator in a potentially dangerous situation with 82.2% precision and an average speed of 110 frames per second (FPS), which fully meet the requirements of a real-time system for dynamic construction environments.

China is actively taking the path of green and low-carbon development, so energy conservation in the construction field has also been put on the agenda, and the energy consumption of residential lighting cannot be underestimated. This paper studies the optimization design of residential lighting by exploring the calculation method of carbon emission of lighting design based on the whole life cycle. Taking the lighting design scheme of Yinyue Wanjing Mansion Project in Foshan City, Guangdong Province as an example, Dialux are used to simulate lighting parameters, and an optimization scheme is proposed to reduce carbon, save energy and optimize cost. A simple carbon emission calculation formula of lighting design based on the whole life cycle is innovatively proposed, which provides a healthy and energy-saving optimization method for lighting design. Finally, it discusses the difficulties encountered in the promotion of health lighting in China, and provides relevant suggestions for residents and designers to solve problems.

Choosing an effective and efficient building design choice for energy management and for optimal cash flow utilisation connected with a building’s life-cycle is always a significant challenge for designers in the effort against global climate change. In addition to reducing greenhouse gas emissions, researchers all over the globe are prospering in the development of building optimization models that would reduce a building’s total energy usage.To foster environmentally friendly development, the Vietnamese government deploys tough law enforcement, well-defined green construction regulations, and a variety of other techniques. To encourage construction enterprises to adopt eco-friendly materials in their current and future projects. According to this study, green building assessments will be influenced by a number of significant factors, such as groupings of variables related to “Experience, credentials, and talents.” Group of variables associated with “Motivation to maximise economic performance of the project life cycle,” “Energy conservation,” “Water conservation,” “Materials & Resources,” “Management,” and “Motivation to promote the benefits of market strategy.” The management of energy efficiency ranks first among the categories of factors that impact the approach and assessment of a green building. In addition, this will affect future evaluations of green buildings, such as life cycle cost analyses and environmental impact assessments undertaken during the development of the project. It is vital to identify and prioritise the components that impact the development of a green building. It will provide more precise project development approaches to academics, designers, and practitioners.

With the unbalanced development of the ecological environment and economic growth, eco-efficiency (EE) has turned into a popular research topic. This paper measures the eco-efficiency values of Chinese cities using the Super-slack-based measure (Super-SBM) model with non-desired outputs. Data for the study were obtained from a sample of 276 cities in China from 2015–2019. The outcomes show that: (1) The top three performers in EE are Beijing, Ziyang, and Zhoukou, and the bottom three performers are Wuzhong, Yichun, and Tieling; (2) the amount of cities in the lowest efficiency group decreases, and the average eco-efficiency value increases slightly overall; (3) by region, the average eco-efficiency value is highest in the east, comparable in the central and western areas, and lowest in the northeast; (4) from the period, the eco-efficiency values in the eastern and western regions have not changed much since 2015–2019, but the central region has increased from 0.28 to 0.42 and the northeastern region has decreased from 0.31 to 0.24. The results of the study provide a significant reference for policymakers to consider environmental protection and economic growth in an integrated manner.

The green development of the construction industry plays an important role in promoting the ecological civilization of China. By applying the SBM model, the global Malmquist–Luenberger index, and the GeoDetector model, this paper examines the green total factor productivity (GTFP) of the construction industry of the Yangtze River Economic Belt. Results show that ① the GTFP of the construction industry of the Yangtze River Economic Belt has demonstrated a growth trend in most years with an average annual growth that exceeds the national level. In the upstream, middle, and lower reaches of the river, the changes in GTFP show great convergence, whereas the technical efficiency and technical progress indices show volatile trends; ② the increase in GTFP is seriously polarized and reflects an unbalanced development in the Yangtze River Economic Belt. The highest and lowest GTFP levels are reported in the downstream and upstream areas of the river, respectively. However, opposite trends are observed for the annual GTFP growth rate. This research is the basic work to further find the driving factors of GTFP growth and formulate the policy to improve the GTFP of the construction industry of the Yangtze River Economic Belt.

The fast-developed artificial neural network (ANN) technique has been used in the architecture, engineering, and construction domain to facilitate the designing process, such as layout planning and structural designing. However, it is difficult to collect sufficient drawings from data owners for privacy reasons and due to the lack of proper incentive mechanism. To address the challenges, this paper proposes an incentive collaboration framework based on federated learning and game theory to enhance data sharing for intelligent design of building projects. Federated learning can train ANN models on data owners’ local devices and thus avoid directly publicising their drawings. A collaboration matching mechanism is designed based on game theory to encourage drawing owners to publicise their data. A case study is conducted using drawings of residential buildings to validate the feasibility of the proposed framework. Results show that (1) Data owners who provide data with higher quality and larger quantity can trade for more data in collaboration; and (2) The ANN model trained through federated learning on larger quantity of drawings performs better than the one trained on local data. Practically, the innovative framework should facilitate the development of intelligent design models with higher performance to assist building designers. Theoretically, the combination of federated learning and game theory could enhance the knowledge of addressing the data sharing dynamics and complexity in innovative construction such as modular building.

Design for manufacture and assembly (DfMA) encourages upstream and downstream construction stakeholders to involve early and communicate openly before developing a manufacture and assembly-oriented design. However, this communication network is understudied and currently challenged by many infection control measures against the pandemic, e.g., lockdown, isolation, and social distancing. This research, therefore, investigates multi-stakeholder intensive communication underneath DfMA implementation and the impact of COVID-19. It does so by participatory action research, tracing stakeholders’ activities, and thematic analysis. It discovers an underlying decentralized mesh communication network, involving the iteration cycle of inquiry and response, submission and feedback, and reporting and acknowledgment. Amid the pandemic, these actions cannot be taken through traditional communication mediums, forcing stakeholders in the case study to adapt multiple generic, virtual platforms to convey various message forms, including technical information and three-dimensional models, without systematic guidelines or integrated platforms for visualization, validation, and tracking. To stabilize this network in the post-pandemic era, the DfMA practice, together with integrated project delivery (IPD) and building information model (BIM), is highly recommended. The communicators, connections, messages, and mediums visualized in this research are valuable resources for governing future practice and developing an integrated platform-empowered mesh communication in DfMA.

Actively fulfilling corporate social responsibility (CSR) will significantly impact the sustainable development of construction companies. Economic globalization has prompted Chinese construction companies to target overseas markets and increase their turnover. The process of internationalization may influence the CSR of Chinese construction companies. This study aims to analyze the effects of internationalization on the CSR of listed construction companies in China. This study tests the hypothesis empirically by multiple regression analysis with the sample of A-share listed construction companies from 2010 to 2020. The results of this study demonstrate that the degree of internationalization (DOI) has a negative impact on CSR. The findings provide suggestions to Chinese construction companies for implementing CSR activities, especially in the context of globalization. Chinese construction companies should pay more attention to CSR activities to promote the sustainable development of the construction industry.

With the development of urban construction, the number of high-rise buildings is increasing, and the diseases of external wall appeared over time. Conventional high-rise building wall inspection is usually inefficient. Unmanned aerial vehicle (UAV) with cameras improves the efficiency of wall inspection, however existing complete data collection method of the whole external wall without any focus is time-consuming. Augmented reality (AR) with simulation can drive the real equipment to execute expected task properly. Therefore, this paper proposes a simulation-driven data collection method of external wall by integrating UAV and AR. This method firstly imports building model and UAV model into AR operation system, and relevant components are added to construct AR interface. After that, the grid map is generated and the voxelized building point cloud data is imported, and the map is integrated into the UAV control system to realize the path search algorithm. Then, a special network connection protocol between Robot Operating System (ROS) and AR is used to link them and realize the control and information transmission between UAV and AR. Finally, a scenario model is built in simulation environment to verify the feasibility of this method. The results show that this method can successfully control the UAV in the AR equipment and obtain the required information and improve work efficiency. This method aims to verify the use of AR to control equipment to achieve intelligent building external wall data collection. In the future, this technology will continue to be expanded and applied in practice.

The current rapid urbanization process has led to an uneven distribution of infrastructure, which has brought about many environmental and social problems. Therefore, it is necessary to have a comprehensive understanding of the construction of urban infrastructure in order to better plan the direction of urban development and cope with social problems such as educational resources, public health, and aging. As a common spatial indicator in urban geography studies, accessibility is an important tool for monitoring and constructing urban development patterns, as well as an indicator of fairness in resource allocation reflecting sociological studies, and the most common method used in the existing literature to evaluate the fairness of infrastructure facilities is also accessibility analysis. The traditional accessibility review is too simple in its methodological analysis, and with the development of information technology, it does not include many new methods in its examination. Therefore, this thesis adopts a systematic review approach to comprehensively analyze the strengths and weaknesses of existing methods, their scope of application, and perspectives of concern, and to gain a clearer understanding of future accessibility method improvements. It is found that the subjective factors of residents, i.e., mobility and consumption level, are rarely considered when examining factors affecting accessibility; current accessibility measures mainly examine accessibility at a certain time slice, i.e., static accessibility, and less research is conducted on dynamic accessibility, which is particularly important for certain facilities, such as emergency medical facilities; accessibility is mainly studied for common green spaces, transportation, and medical facilities. Therefore, accessibility measures are more oriented to spatial accessibility, and less attention is paid to non-spatial accessibility.

China's urbanization has entered a stage of renewal and quality development, and urban construction pays more attention to connotation development. Urban renewal planning is not only based on the improvement of its own spatial functions, but also plays a positive role in improving the overall quality of the city. This paper briefly expounds the research background of this topic, focusing on two aspects: the construction of high-quality urban public space and the implementation of the high-efficiency collaborative model of public value, and analyzing and studying the urban renewal planning. In addition, two planning modes of refined improvement and micro-renewal are proposed in the implementation of urban public space renewal, and the implementation path of urban renewal construction that is conducive to public value orientation is explored.

At present, the selection of construction solutions for highway construction projects mainly relies on personnel experience for the preparation of construction solutions for target projects, so to make use of the experience of previous highway projects, this paper establishes a decision model for construction solutions of roadbed projects based on case inference techniques. First, several characteristic attributes are extracted from the previous literature and data. The item attributes are extracted from the selected historical items, and the data is dimensionless. Moreover, the characteristic weights are assigned to the attributes using the entropy weight method. Based on this, the search for similar items is completed using gray correlation analysis and the items with the highest similarity are selected. In the last step, experts refer to the projects with the highest similarity after conducting the screening to prepare the roadbed construction plan for the new project. Finally, the model was applied to the reconstruction project of the National Highway G350 Huangshui-Huashi Ling section road in Shizhu County. Nine historical projects in the southwest area were selected for screening. The final results showed that the selected historical projects were mainly similar to the construction schemes of the target projects, which proved the model's effectiveness and effectively improved the efficiency of the roadbed construction scheme preparation.

Family migration has become the main migration pattern of rural-urban migration in China, which is characterized by phasing and dynamic. Previous studies have focused on the impact of housing on individual migration, paying less attention to family migration, and few studies have examined the impact of housing in different stages of family migration. This paper constructs a staged and dynamic decision-making mechanism based on the Todaro model, which divides family migration into two stages: family separation stage and family reunion stage, and uses data from the 2017 China Migrant Dynamic Survey for analysis. The following conclusions are drawn: housing cost has a significant negative impact on rural-urban family migration of renting households and a significant positive impact on rural-urban family migration of purchasing households. The effect of housing ownership on family migration is significantly positive and mainly plays a role in the family reunion stage. The effect of relative housing deprivation on family migration is significantly negative. The results of this paper will help formulate housing policies that are more compatible with family migration to serve new urbanization.

TOD (Transit-Oriented Development) has been widely accepted in many countries as an important spatial strategy to guide the coordinated development of rail transit and urban spatial structure. However, there is still a lack of evaluation and understanding on the TOD practices in the Chinese cities. Based on site-level land use data of Tianjin in 2019, this study aims to fill this gap by an empirical investigation on the effects of rail transit stations on land use outcomes in Tianjin, China. Land use types are identified within 500 m of rail transit stations in Tianjin. Shannon entropy is used to measure the land use diversity of these areas. Multivariate linear models are established to explore the quantitative relationship between various factors and land use diversity around stations. Our study shows that land use types around transit stations are mainly residential and commercial land. Land use diversity shows a great variation around transit stations in the central and suburban area. According to our results, The density of public transportation and the distribution of schools are positively related to land use diversity. The aggregation of residential land is negatively related to land use diversity around stations in the central area. As for the stations in non-central districts, the aggregation of business land has a significant and positive effect on the land use diversity of the station areas.

With the tremendous changes in the domestic and international environment, the phenomenon of shrinking cities in China has gradually attracted attention. This paper mainly analyzes the law of shrinking cities based on catastrophe theory and system dynamic feedback theory, constructs a research model of shrinking cities to deduce its future development trends based on the ten-year historical development trajectory of shrinking cities. Taking two Chinese shrinking cities as examples, the general laws are deduced from three aspects: manifestation, motivation mechanism and evolution mechanism. The form of urban contraction in China is that the economy shrinks first, then the population shrinks, and finally falls into a vicious circle of low efficiency locking. The main reasons for urban shrinkage are single industrial structure, aging economic structure and blind deindustrialization. China’s shrinking cities are facing long-term, irreversible shrinkage.

This paper analyses the impact of using battery storage in solar PV homes. It uses actual PV generation data and smart meter data from a case study of a house in Geelong, Australia, to study this. As the adoption of intermittent solar photovoltaic (PV) systems grows, storage capacity, such as batteries, is required to match unpredictable generation with uncertain demand. The results show that applying a 10 kWh battery to a 10 kW solar PV system can reduce annual imported energy by 95%. In order to make the house grid independent, a 20 kWh battery is required with a payback period of 25 years. In addition, PV self-consumption and PV self-sufficiency rise as the battery capacity increases, but this trend is limited by constrained PV generation in winter. This study demonstrates the feasibility of applying battery storage in solar PV homes, but the characteristics of PV generation and house power demand need to be considered to determine the best combination of PV and battery system size.

Urban regeneration provides opportunities to address urban problems in order to achieve sustainable urban development, which has received significant attention worldwide. As the practice of urban regeneration practice is a complicated system engineering, inappropriate urban regeneration strategies may cause huge economic loss, exacerbate social problems, and even hinder long-term development for urban sectors. Scientific decision-making on urban regeneration before its implementation has gradually been regarded as one of the most effective solutions to such a challenging issue. Although existing studies have begun to pay attention to the issue of urban regeneration decision-making, an integrated and systematic review covering the whole decision process has yet to be produced. To overcome the gap, using 293 articles which were published between 1968 and 2022 collected from the Web of Science Core Collection Database, a bibliometric analysis offers the overall development and hot research topics of the existing research in urban regeneration. To better clarify the mechanism behind the urban regeneration decision-making, based on the bibliometric analysis’s results, this paper proposes a novel conceptual decision-making process framework for urban regeneration and critically reviews the previous theoretical landscape in terms of the five components of the framework, namely decision objectives, decision content, decision-makers, decision methods and decision results. The paper also outlines pathways and provides recommendations for future research directions.

Real Estate Investment Trusts (REITs) has caused great concern for its capacity of serving the real economy with the capital market. The research related to REITs is more and more, especially in China. It is vital for interested researchers to get full picture of global research. However, the current review in the field of REITs mainly focuses on the summary of single area of REITs, and there is a lack of systematic review of other latest research progress in this field. This paper reviews the current REITs research with 817 academic papers from Web of Science database by scientometric analysis techniques. Visual analysis of authors, affiliations, countries, and keywords through CiteSpace software reveals the main areas and future trends in REITs research. Based on this, 4 major themes are identified, that is, risk and uncertainty management, capital structure, investment performance, price volatility. Additionally, a new AIC (Asset-Information-Capital) framework is also proposed to structure existing REITs research. Finally, 3 specific future directions are presented to comprise more attention on social performance of REITs, asset choice with environment-oriented configuration, the match between governance and regulation of REITs. Therefore, this paper provides a critical review of the extant REITs literature, and also contributes to the development of REITs practice in China.

The development of rail transit is expected to gather housing and jobs around the station, so as to improve the urban spatial structure for sustainable development. Based on mobile phone signaling data in 2019, this study analyzes the coupling relationship between metro station areas and urban population intensity in Tianjin. Regression models are established to quantitatively evaluate the effects of metro stations on the residential population density and employment density around the station. The study shows that the residential population and employment are significantly concentrated along the rail transit area. The employment density, the proportion of residential land and commercial land are the most influencing factors in residential population density within the station surrounding areas. Whereas, the residential population density, average and house price and the proportion of industrial land are in employment density within the station surrounding areas. Based on the research results, policy implications are proposed to better guide the coordinated development of rail transit and urban spatial structure.

The success of international construction projects depends on the effective performance of construction expatriates. However, cultural issues often hinder their performance in the new overseas working environment. To address this challenge, organizational supports in the process of adaptation and integration are crucial, but often overlooked. This study aims to develop a research model linking cultural intelligence to the individual work performance of construction expatriates by considering other constructs that contribute to this association based on a systematic literature review. Our findings suggest that cultural adjustment plays a mediating role in the relationship between cultural intelligence and individual performance, while perceived organizational support may have a moderating effect. The proposed research model provides a framework for further analysis, enabling organizations to better support construction expatriates in their adaptation and integration process.

Although there are a few empirical research into the dispute problem in the Chinese construction industry, the causes and effective resolution methods are unknown in the context of COVID-19 pandemic. To investigate the causes and resolutions of construction disputes in China, this research employs the methods of literature review, questionnaire survey and interview. 93 professionals’ viewpoints were used for empirical analysis. The results indicate that the COVID-19 (4.20) has a significant impact on construction disputes, and contractual causes (4.00) are the most common disputes. Diverse stakeholders’ perceptions of the causes of construction disputes varied significantly. Furthermore, most disputes are resolved by negotiation (35.5%) and mediation (49.5%). This research provides professionals with invaluable insights into construction dispute’s types and dispute resolution systems. Moreover, it allows the various construction stakeholders to understand and take into consideration the perceptions of other participants when applying risk and dispute prevention techniques.

Market sentiment is an important factor affecting real estate market transactions. In the face of irrational fluctuations in China’s real estate market, “Expectation Stabilization” is regarded as one of the important policy goals of real estate regulation. This paper takes Chongqing as an example to make an in-depth analysis of the impact of market sentiment on the second-hand housing market. Firstly, obtain the corpus of Chongqing real estate industry information from 2019 to 2021 based on the Sina public opinion platform, analyze the sentiment tendency of the corpus and construct the market sentiment index accordingly. Secondly, establish the VAR model of the second-hand housing market by adding variables such as real estate development investment and M2 money supply. Finally, make an empirical research on the impact of market sentiment on Chongqing’s second-hand housing market. The results show that market sentiment has a significant impact on the second-hand housing market in the short term, and there is a hysteresis. Faced with the impact of market sentiment, the response of second-hand housing transaction area is more significant than second-hand housing sales price.

For public health emergencies, adopting different response plans often makes the emergencies develop in different ways. In this paper, we propose a methodology for emergency decision-making based on regret theory, which takes into account the preferences of decision-makers. In this paper, we first calculate the utility value of each solution (including the utility value of the loss caused by the emergency and the utility value of the cost incurred in implementing the solution); then, we calculate the regret value of the emergency solution to obtain the perceived utility value of the loss and cost incurred by the decision maker; then, we calculate the combined perceived utility of the emergency solution; then, based on the combined perceived utility, we add the decision maker’s Finally, an example study is presented to show that taking into account the decision maker’s preferences does have a significant impact on the choice of contingency decision.

Construction inspection is critical to ensuring the quality and long-term performance of highway infrastructure. In the current practice the inspectors need to manually gather and interpret the lengthy quality standards for inspecting and evaluating the highway construction work, which is subjective, error-prone, and time-consuming. Many transportation agencies have developed inspection forms to organize construction requirements that reside in textual documents (e.g., standard specifications, construction inspection handbooks, and quality standards) in the format of checklists in order to reduce the workload for inspectors and enhance productivity. However, due to the missing link between the inspection forms/checklists and the construction work under inspection, the inspectors might need extra work to find the applicable forms/checklists for inspection. This paper thus proposes a Building Information Modeling (BIM)-based approach to establish the missing link. First, the semantic structure underlying the quality standards for highway construction (a Chinese quality standard for highway engineering is selected as the case) is analyzed, followed by the re-structuring of the quality requirements as inspection forms. Then the inspection forms are associated with their relevant BIM objects. Last, a BIM-enabled prototype is presented to illustrate the generation of customized inspection forms as construction progresses. With this newly developed tool, field inspectors can get rid of the overwhelming texts in the quality standards and can be equipped with the necessary knowledge regarding what, when, and how to inspect.

Virtual annotations (VA) for compromising visual limitations during construction equipment teleoperation can provide critical instructional information, but it remains to be verified whether an operator can respond to VA timely and accurately while operating. Studies have demonstrated a positive relationship between spatial perception ability (SPA) and teleoperating performance. Many studies considered that those with SPA have a lower mental workload and more attention on other matters. Therefore, this study sought to examine whether an operator with a higher SPA score can respond better to VAs. Quantitative results from the SPA test questionnaire and the teleoperated excavator experiment showed a significant inverse relationship between SPA and the correct response time. It is expected that the results could improve the effectiveness of selecting construction equipment operators and optimize prior management.

As urban rail transit disasters present network transmission characteristics, it is imperative to identify the transmission mode and key nodes between disasters to prevent and control urban rail transit system risks. However, the failure of urban rail transit equipment, as the most common and most types of failure disaster events during the operation of urban rail transit, has not been deeply studied and analyzed. Therefore, this paper uses the inductive analysis method and the catastrophe chain transmission theory to construct the evolution of the rail transit equipment failure disaster chain Model and complex network of rail transit equipment failure disaster transmission evolution. Based on complex network theory, collect and summarize various urban rail transit accidents related to equipment failures at home and abroad in recent years, study the chain transmission law of rail transit equipment failure events, and use various related indicators to determine the importance of network nodes. The vulnerability of the edge and the edge are comprehensively analyzed, and the risk value of each rail transit equipment failure disaster chain is obtained. Through quantitative analysis, the risk assessment of rail transit equipment failure disaster chain provides a basis for rail transit operation safety assurance and disaster prevention and control.

Metro construction is characterized by numerous safety risks. Although risk transmission widely exists during metro construction, how to scientifically evaluate the risk transmission strength is still vague. This study aims to address the complex transmission interactions between safety risk factors of metro construction. First, the definition of risk transmission was explored. Then, the combined interaction of causal relationship and coupling relationship among safety risk factors were unraveled using the DEMATEL (Decision Making Trial and Evaluation Laboratory) technique. The transmission degree was calculated based on the sum of causal centrality and coupling centrality. It is found that the transmission strength is concentrated in the region of [0, 0.6], indicating the interactions among safety risk factors of metro construction are strong in China. Moreover, the most strong transmission factors are revealed. This paper contributes to improving the accuracy of safety risk assessment in the metro construction field.

Disasters has implications for the sustainable development goals and affect the functional performance of critical infrastructure. Public-private partnership is recognized as a medium to build the resilience of critical infrastructure. While previous literature provides valuable insights into public-private partnerships in building critical infrastructure resilience, key performance indicators for a public-private partnership in critical infrastructure resilience is yet to be established. In this study, a systematic review was conducted to propose a set of KPRI in PPP in critical infrastructure resilience. Using a three-stage literature search, 95 publications were selected for content analysis. A total of 23 key performance resilience indicators were proposed and subsequently grouped under five categories namely, System performance, Human resource, Social, Record Keeping and Equipment KPRI. Considering this study as a pioneer, this study will provide an in-depth information and knowledge needed for stakeholders in critical infrastructure resilience such as policymakers and infrastructure operators in building the resilience of critical infrastructures.

Decoration and renovation waste (DRW) recycling is one of the most important solutions to maintain economic development while minimizing environmental contamination related to DRW. Homeowners play a pivotal role in DRW recycling management. However, there is a serious lack of research on DRW recycling behavioral intention of homeowners. The purpose of the current study is to explore the critical factors affecting homeowners’ DRW recycling intention and their mechanisms from a perceived value perspective. A new model that covered environmental values and perceived value was developed, and subsequently was examined via the partial least squares structural equation modeling (PLS-SEM) based on the questionnaire data of 155 respondents. It is found that the proposed model provided an effective framework for explaining homeowners’ intention to participant in DRW recycling. Results revealed that homeowners’ DRW recycling intention was affected by environmental values as well as perceived value. Moreover, perceived value played a significant and positive mediating role between environmental values and DRW recycling intention. Therefore, to enhance homeowners’ intention to engage in DRW recycling, several measures should be taken by both neighborhood committee and government, e.g. launching an advocacy campaign and providing economic supports.

Rapid urbanization in China has caused the serious urban heat island (UHI) effect, which is endangering the environment where people live. Many studies have demonstrated the significant contributions of two-dimensional (2D) and three-dimensional (3D) urban spatial form indicators to UHI, but further research is expected to conduct given that UHI has spatial non-stationarity and spatial spillover effects of explanatory variables. Spatial regression models are particularly appropriate to solve the spatial non-stationarity problem. This essay seeks to analyze the impact of urban spatial form on UHI taking the spatial spillover effects into account. In this paper, Landsat 8 OLR/TIRS satellite image was used to invert surface urban heat island intensity (SUHII) in the central city of Hangzhou, China. The Moran's I index for SUHII was 0.457, indicating a substantial positive spatial autocorrelation. After the establishment of 2D/3D urban spatial form indicators at the block scale level, the influence of 2D/3D urban spatial form indicators on SUHII w`as examined using the spatial lag model (SLM), spatial error model (SEM), spatial Dubin model (SDM), and spatial Dubin error model (SDEM), respectively. Results suggest that SDM and SDEM fit better by considering spatial interaction of urban spatial form indicators. The coefficients for the ratio of impervious surface area (RISA), ABD (average building density), and sky view factors (SVF) were significantly positive, while the effects of the landscape shape index (LSI), average building height (ABH), average forest height (AFH), and normalized difference vegetation index (NDVI) were significantly negative on the SUHII. Additionally, there were spatial spillover effects of LSI, SVF, AFH, NDVI, and SUHII affecting SUHII. The findings imply that the urban form indicators and SUHII will affect the SUHII of adjacent blocks, so it is important to make suitable urban 2D/3D shapes to enhance the urban thermal environment. These findings would be helpful for urban planners to mitigate future UHI effects.

In recent years, the Chinese government has continuously issued relevant policies to promote prefabricated buildings in the construction industry, but there are some issues, such as the manufacturing link of prefabricated concrete components (PC components). Due to professional prefabricated components manufacturing robots usually need to be imported in complete sets, the price is extremely expensive. The manufacturing process of PC components is still largely manually. Large numbers of workers gathered in closed factories are very dangerous under the epidemic. This study summarizes and sorts out the production stages of various PC components. This paper collected intelligent professional robots commonly used in the production of PC components, and summarized them into four platforms: robotic arm, mobile car, parallel axis, and fixed type. For the industrial robots which are very mature, this paper explores the application opportunities of them in the production of PC components through secondary development. In this way, the use cost is lower, the layout is more flexible, and it is conducive to the purpose of large-scale application.

As the construction industry’s flagships, mega-infrastructure projects are often involved in advanced practices. In these practices, innovative efforts are critical to the sustainability of mega-infrastructure projects. In the past decade, much research has been conducted on innovations in mega-infrastructure projects. However, an in-depth understanding and knowledge guidance in this field is still lacking. Therefore, it is essential to carry out a thorough review to identify current research objectives and potential future paths for research development. In this study, the Scopus database was used to extract articles published in the past 10 years (i.e., 2012–2021) to reveal the knowledge state and knowledge gaps of innovations in mega-infrastructure projects and to clearly and objectively analyse their knowledge structure and relationships. To achieve these goals, a mixed-review approach was adopted, which includes scientometric and systematic analysis. The scientometric approach identified the most influential papers, the co-occurrence network of authors, and keywords clusters. Moreover, the systematic analysis divided the articles according to the research dimensions (antecedent, content, and impact) of innovations in mega-infrastructure projects. The results show that innovation in mega-infrastructure projects has gained more attention over the past decade. In addition, the majority of studies on innovations in mega-infrastructure projects concentrate primarily on the nature of innovation itself, often ignoring the influencing factors of innovation and the impact of innovation. These findings provide practitioners and researchers with a clearer understanding of innovations in mega-infrastructure project and Inspire future research based on identified gaps.

As China’s urbanization enters a rapid development stage, the expansion of cities has generated several problems. The road network influences urban form significantly and can contribute to sustainable urbanization. Currently, studies on urban expansion rarely use road networks. This study aims to analyze the spatial aggregation and intensity of urban expansion using road network data by space syntax. The results of spatial autocorrelation analysis show a moderate degree of positive spatial autocorrelation in the main urban of Hangzhou. The high–high agglomeration regions grew gradually and were primarily concentrated in the center, whereas the low–low agglomeration regions were scattered around the boundary. According to the Space Syntax Expand Intensity Index (SS-EII), the main direction of urban expansion was the northeast and north, while the southeast and east expanded at a slower rate. Moreover, the overall intensity of urban expansion was highest in 2018. Finally, the reasons associated with urban expansion are explored, including demographic, economic, policy, and geographical factors.

The relational behavior of participating organizations is vital to improving megaproject performance. Although extant research has focused on promoting relational behavior by identifying various factors, it neglects the mutual influence of participating organizations to make relational behavior adoption decisions, especially considering the heterogeneous reciprocal preferences of distinct organizations. To fill this gap, this study constructs an evolutionary game model between two parties with different reciprocal preferences to analyze the evolutionary stability of each organization’s choice. We discuss and simulate the impact of initial relational behavior adoption willingness and reciprocal preferences of different participating organizations, resulting in both factors having a positive effect on the adoption of relational behavior. We further tested their relational behavior choices under different work scenarios (i.e., complex subproject, innovative subproject, interdependent subproject, and simple subproject) in megaprojects. The results are that the first three scenarios all end up with relational behavior adopted by both sides, while the last scenarios render non-adoption of relational behavior by both sides. This research promotes a better understanding for the dynamic mutual influence of participating organizations in megaprojects and unveils the driving paths to enhance relational behavior adoption from a perspective of organizations’ reciprocal preferences.

In the post-epidemic era, the construction industry has been greatly impacted, and the wave of labor shortages in the construction industry has reappeared. The construction industry has frequently faced the problems of “labor shortage” and “difficulty in recruiting workers”. At the same time, with the accelerated development of China’s population aging process, research on the job satisfaction of construction workers, has certain practical significance for improving the employment happiness of construction workers and retaining the labor force in the construction industry. First, through literature research and on-the-spot investigation, based on 6 dimensions: work itself, work environment, public services, rights protection, leaders and colleagues, salary and welfare, 20 indicators were selected to establish a job satisfaction of construction worker evaluation index system; Secondly, the improved AHP-FCE (three-scale analytic hierarchy process-fuzzy comprehensive evaluation) is used to determine the weights of indicators at all levels, and an evaluation model for construction worker’s job satisfaction is built. Finally, the feasibility of the evaluation system is verified through empirical research, and suggestions on improving the job satisfaction of construction workers are put forward in combination with social reality.

Modularization has become an important strategy to support current enterprises in gaining an edge in a competitive market. The construction industry is also continuing to explore the same trend. Modular integrated construction (MiC) in Hong Kong is one of these notable examples. MiC has been vigorously promoted by the Hong Kong government as an innovative construction method. However, MiC has some features that make it challenging to adopt, e.g., a wider range of stakeholders and a longer supply chain compared to the traditional construction method. This means that the requirements of all parties and subsequent stages should be considered early in the design phase. Modularization, as a critical aspect of MiC design, should also be carefully carried out to achieve smooth construction. This study, therefore, adopts a case study method to explore the modularization considerations in a real-life MiC project in Hong Kong. Four major considerations are summarized, including design with repetitive module units, compact dimensions of module sizes, lightweight module units, and layout adjustment of the structural core. This study will contribute to the design theories and practices by providing evidence-based modularization considerations in modular building design.

As the construction of emergency projects is of great significance for pandemic control, it has received special attention in the project management realm. Emergency projects possess special complexities that distinguish them from general projects, while extant research focuses on rapid construction techniques but ignores project complexity and its management. In this regard, this study identifies the various aspects of the emergency project complexity and figures out the adaptive strategies. Based on the complex adaptive systems (CAS) theory, a typical emergency project in Shanghai, China, named the National Exhibition and Convention Center (Shanghai), has been selected to demonstrate the process of complexity identification and the adaptive strategies extraction through two-stage content analysis. The results suggest that the complexity of the process of emergency project construction involves six dimensions: goal complexity, organizational complexity, task complexity, streamline complexity, professional systems complexity, and environmental complexity. Based on the CAS theory, adaptive strategies can be divided into two categories: proactive and reactive behavior, which should be combined to deal with the aforementioned complexities. This research promotes a better understanding of the complexity of emergency projects and extracts the adaptive behaviors to handle project complexity from the perspective of contractors, to help speed up the construction schedule.

With the global warming and national dual carbon target in China, various carbon emission reduction models and methods at the macro level have been explored. CGE model is one of the main research models to solve environmental protection problem and also is a comprehensive research method. Two of the important applications of CGE model are in the environmental and carbon trading fields, respectively. The relevant literature on the CGE applications in the environment and carbon trading fields in Web of Science was first retrieved and then was analyzed by using literature visualization tool CiteSpace, and finally the current situation of application was reviewed. Correspondingly, in the field of construction, the CGE model has been adopted in environmental impact assessment at the macro level. But it is relatively less used. The main difficulty lies in the huge data demand, the limited abstract and expressions of the real economic situation, and linking the bottom-up micro-analytical model characterizing the construction sector with the top-down CGE model. Based on the research status of the application of CGE model, it can be indicated that the CGE model that represents the industry-related impacts and ripple effects will provide feasible research methods and models for future carbon trading research in the construction sector.

As the risks in construction phase of public building green renovation projects are complex, a reasonable evaluation of the risk hierarchy, identification of key risk relationships, and analysis of risk transmission paths make an important foundation for making efficient risk control measures. In this study, 23 risk factors in the construction phase of public building green renovation projects were firstly sorted out, a four-level hierarchical structure of the risk system was derived by applying the ISM modeling method, combing with the MICMAC method to calculate the driving forces and dependencies of risk factors. And to divide the risk factors into three clusters: independent cluster, autonomous cluster, and dependency cluster. The results of the study can provide reference and decision-making ideas for risk management of green retrofit projects in public buildings. The model results show that risk factors including the policy, supervisory responsibility, technical management experience, personnel safety awareness, and equipment operation risks, as deep-level influencing factors, have a high driving force and strong influence, and should be actively controlled as risk sources. Contract and related management risks, which are at the surface level, are not only susceptible to the influence of other levels in the system but also have complex interactions within the levels.

Urban greenspace is an effective approach to reducing urban heat island (UHI) effects and providing comfort to the nearby occupants. In this study, a multiscale evaluation of the cooling effect of different types of greenspaces was conducted. Studies were conducted among four typical urban communities in two densely urbanized cities - Hong Kong and Singapore. Large-scale land surface temperature (LST) of pixel-level were retrieved from cloud-free satellite images. Long-term hourly meteorological data collected from weather stations was used for validating the LST retrieval results. Based on LST data at the macro-scale, and microclimatic modeling using simulation tools (ENVI-met) at the micro-scale, this study evaluated the performance of different types of greenspaces in cooling the environment of several typical urban communities. For the street-level greenspace, how the major characteristics of greenspace affect the cooling effect of greenspace was identified. Results demonstrate that the cooling effect of greenspace improves logarithmically with an increase in its size and density. Especially, the leaf area index ( $$LAI$$ LAI ) plays a major role in cooling the surrounding environment with the R2 between $$LAI$$ LAI and the maximum local cool island intensity (MLCII) reaching 0.30 in Hong Kong and 0.33 in Singapore. For the green infrastructure, the simulation results indicate that street trees, green façades, and extensive/intensive green roofs are helpful in reducing air temperature and improving outdoor thermal comfort in the daytime. Among all scenarios, street trees are most effective to reduce air temperature and improve outdoor thermal comfort at the pedestrian-level. In densely populated urban areas, green infrastructure (especially green façade) may be a suitable choice for UHI mitigation, thermal comfort improvement, and cooling energy savings.

Learning the residents’ perspectives on living houses and their satisfaction feedback is meaningful for enterprise reputation service. Some channels (systems of surveys) have already been established in our daily life to help residents publish the residents’ satisfaction feedback. However, related satisfaction feedback data’s authenticity and integrity cannot be guaranteed in centralized architecture channels currently, which limits the authenticity of reputation judgment of related enterprises (e.g., the realtor company, the tenement company, and others) building organizations. To track the issues, a blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residence satisfaction feedback. The framework involves two parts: (1) building a residence satisfaction evaluation model; and (2) defining one type of smart contract to create an interactive and efficient feedback-sharing method in a decentralized blockchain architecture, which could effectively achieve the goals of transparency and trustworthiness. This study provides a novel solution for understanding the residence satisfaction with living houses with trustworthiness consideration, inspiring more discussions about blockchain technology.

Affected by the characteristics of the construction industry, construction practitioners suffer from many job stressors, leading to high risk of poor mental health. By adopting a mixed research method, this paper aims to identify construction practitioners’ job stressors and unravel the relationship between job stressors and mental health status. A literature review, a questionnaire survey, and interviews were conducted to achieve the research objectives. Factor analysis results indicate seven major job stressors among Chinese construction practitioners, including: job demand, welfare and social economy, workplace injustice, personal and interpersonal relationship, work-family conflict, job role and workplace condition. Descriptive analysis results show that construction practitioners had serious depression (56.5%) and anxiety (51.9%) problems, but the stress condition (22.1%) was in a moderate level. Furthermore, the results of the correlation analysis reveal that the key job stressors affecting construction practitioners’ depression were work-family conflict stressors (0.591), personal and interpersonal relationship stressors (0.577) and welfare and social economic stressors (0.531), whereas the main job stressors affecting construction practitioners’ anxiety were personal and interpersonal relationship stressors (0.601) and work-family conflict stressors (0.578). The findings of this paper will serve as a theoretical foundation for construction companies to implement efficient mental health management strategies for their employees.

Housing market regulation is a regular policy tool globally while it manifests uniqueness in China at the meantime. Chinese state government has initiated a number of constrained policies on housing market since 2011 and the policy intensity changes from time to time. Most notably, a wide range of differences can be witnessed within the housing regulation policy content across cities. Noticing that past research efforts have focused on the multifold consequences of regulation policy, it’s necessary to explore the heterogeneous local responses to housing market regulation, and the underlying reasons. This paper establishes a framework to interpret the variation of housing regulation policy across cities, including enabling factors, constraining factors and the balance of the two. An index ‘regulation policy intensity’ is proposed to measure to what extent local policy intervenes local housing market. Text analysis and regression examination are explored to achieve the above research goals. The results show that: under the risk of economic downturn, a city doesn’t tend to propose high-intensity restriction policy when its finance heavily depends on land revenue. Cities with higher house price growth rate, higher housing-price-to-income ratio, higher real estate investment accounts and named by the central government to regulate housing prices are more likely to propose stricter regulation policy content. These factors relate to the external pressure a city faces in its decision making. The differentiated local regulation policy intensity reveals the compromised outcome of local government between political pressure and development risk.

Adaptive project scheduling is paramount for project success. However, it is challenging for industrialized construction (IC) projects to their fragmentation with spatial-temporal distributed work packages (e.g., tasks in production, transportation, and on-site assembly). To achieve adaptive project scheduling in IC, this study proposes a double deep Q-network (DDQN)-enabled two-layer adaptive work package (D2-TAWP) approach. First, the project scheduling process is transformed into a Markov decision process to model the sequential decision-making process of scheduling; Second, a two-layer adaptive scheduling approach is developed to schedule tasks of work packages dynamically. Finally, the effectiveness of the D2-TAWP approach is validated by experimental simulation. The results indicate that the D2-TAWP approach can effectively perform work package scheduling compared to traditional heuristics, which paves the way for the next-generation distributed scheduling of IC projects.

Construction project managers are under high level of job stress today because of the characteristics of contemporary construction projects, such as large investment, tight project schedule, high requirements and significant influence on the society and public. Different types of construction project managers have been under different job stressors. Based on literature review, this paper adopts questionnaire survey and interview methods to identify the job stressors experienced by Chinese construction project managers and analyze the differences of various construction project managers’ job stressors. The results indicate that there is no significant difference in job stressors for construction project managers with different genders. Significant differences exist in the career-development job stressors of construction project managers with different ages. In addition, there are significant differences in the job stressors of the organizational structure of construction project managers in different organizations. The research results can provide reference for the job stress management of construction project managers in the Chinese construction industry.

In recent years, the amount of commercial concrete increases year by year, many concrete wastes cause the waste of resources, but also increases the production cost of commercial concrete companies. Aiming at concrete order, production, transportation and installation of the whole production process, through literature study and expert interview, identify 31 waste, the impact of factors, questionnaire, and then use SPSS20.0 software for recycling of 211 valid questionnaire summary and statistics today, using the factor analysis method to draw eight main influence factors, According to the weight, the importance of each factor is sorted, and corresponding suggestions and improvement measures are given, so as to provide reference for the source reduction of construction waste and realizing industrial optimization and transformation..

In the context of the “carbon peak and carbon neutral” goal, promoting energy carbon emission reduction in the construction industry has become an urgent problem. However, there are few studies on energy carbon emissions in the construction industry, especially the impact of technological progress as an essential productivity factor on energy carbon emissions is unclear. This paper measures the carbon emission rebound effect of the construction industry based on technological progress from 2002 to 2019 with the DEA-Malmquist index method to measure the total factor productivity. The results show that the rebound effect from 2002 to 2019 is a partial rebound phenomenon, and the effect from 2014 to 2019 is more evident than before. Therefore, it indicates that the rebound phenomenon of carbon emissions in the construction industry should be paid attention to, and there is still much room for improvement in the emission reduction work. Because of this, reasonable carbon emission reduction policies should be formulated to reduce the rebound effect of technological progress on energy carbon emissions in the construction industry.

The profound and lasting influence of Covid-19 and the global consensus of carbon peaking and carbon neutrality is reforming the underlying logic of economic development, and the green economy will become a new engine of economic growth in the future. Government guiding funds (GGF), as a policy tool in guiding industrial development, are playing an important role in stimulating low-carbon green economy growth in China. GGF is another approach different from PPP to harness the collective efforts of public and private sectors, in order to transform the industrial chain, innovation chain and value chain of the urban economy and improve economic resilience. A green GGF selects and financially invests the green startups in the market-oriented approach, yet under the government guidance and regulations, and it will withdraw from the investment when the green business fails or provides sufficient return. Taking the national green finance pilot city of Quzhou in Zhejiang Province as the case, this study adopted documentary analysis and semi-structured interview to identify the operation mechanism, strengths, and weaknesses of government guidance funds in developing green industries and improving urban economic resilience. Based on the experiences and lessons from the case study, some policy suggestions are provided for the improvement of government guidance funds in other regions in promoting green economy development.

With the intensification of the modernization process, the capacity of individual cities has gradually become single, and city groups have replaced it. City group emerged later in China, so there is less experience with the problems that may arise. Scientific understanding of city groups is an essential process of rapid economic development. Only by scientifically building and developing city groups can they play their role. In this paper, the Chengdu-Chongqing Economic Zone is analyzed, and a highway network is constructed in 34 urban areas. The degree of Centrality, betweenness centrality, and closeness centrality are comprehensively sorted. The current policy is compared to analyze whether the current construction meets the development needs. Divide the 34 urban areas into three levels and compare the seven economic belts: In the development of highway infrastructure in thirteen economic belt cities, the infrastructure construction of Leshan, Meishan and Yibin expressways is still insufficient, and it needs to be built in the future development and construction.

Fine-grained spatial information is critical when conducting spatial security assessment, as people’s spatial cognition is derived from specific images of space. Spatial cognition guides people’s behavior in space, and these behaviors pose a hazard to space security, indicating that the assessment of space security should be three-dimensional. Space syntax has played a significant role in studies of the spatial distribution of burglaries as a tool for linking spatial correlation and human activity. However, these studies remain at a two-dimensional neighborhood-level, unable to adapt to increasingly complex spatial structures. In this paper, a framework of indoor space security assessment model considering semantic information is proposed. The new model takes into account neglected spatial semantic information (e.g., opening windows, air conditioning ducts, floor height), all of which have a decisive influence on human behavior patterns and are available in the Building Information Model (BIM). The results show that lobbies, rooms with low floors and with windows, open corridors have a higher integration and connectivity value, which differs from the 2D results, but is closer to reality. This study contributes to the quantitative assessment of interior spaces in complex buildings and helps to rationalize surveillance arrangements and police configurations.

Due to the recent rapid development of smart tourism technologies such as cloud computing, big data, Internet of Things applications, and social networking services, the concept of smart tourism cities has emerged. Research on the topic of smart tourism city construction is also increasing. This study uses bibliometric methods to summarize the literature research in the field of smart tourism cities based on the CiteSpace V and VOSviewer 2020 platforms. This study also conducts a comparative analysis of the research literature on the smart construction of the tourism six elements in a city. Finally, the research trends and hotspots of smart tourism cities are revealed to provide a reference for the development of this field.

With the increasing number of aged buildings, the green retrofit of public buildings is effective way in urban renew process. In order to study the risks of existing public building green renovation projects, this study develops an improved FMEA (Failure mode and effect analysis) analysis model to quantitatively assess the risks. Experts in construction industry are invited to assess the risk factors of public buildings and green retrofit projects, and to determine the weights by entropy weighting method. In the improved FMEA model, the risk factors assessment results quantified by using the Likert five-level scale method, and the EWGM (exponential weighted geometric mean) method is selected to rank the RPN. Based on an empirical study in Chongqing, the top 5 risk factors of the public building green renovation projects of are obtained. Focusing on the assessment results, the possible potential causes of each high risk factor are analyzed, and corresponding risk response measures and suggestions are proposed. The research findings provide references for improving the risk management level of the green renovation project of public buildings.

In the process of prefabricated bridge construction, it is very important to quickly and accurately lift and place prefabricated columns. Column assembly mainly controls the butt joint between the column sleeve and the reserved rebar of the cap. Before assembling, to test the length and distance of rebars reserved for each cap is of necessity and the deviation should be controlled within 5 mm. Whereas the work is generally conducted manually, which is low-efficiency and error-prone. Therefore, this paper presents a method for bridge cap rebar to automatically detect the position, spacing and length of rebar using raw scanned point cloud data. The BIM model of bridge cap rebar is transformed into point cloud, the average length and the diameter and distance of rebar are then automatically calculated. A method combines multi-plane segmentation and pass-through filtering based on the parameter from BIM model is developed to remove members that are not rebar. Furthermore, a minimum 3D bounding box method is used to extract the length and center of the rebar, through which the distance of the rebar can be calculated. Experiments on a prefabricated bridge pile cap are carried out. The comparison results show that the root mean square error of the length and spacing of the steel bar between the method and the manual detection is 2.460 mm and 1.214 mm, respectively. The results show that the proposed method can accurately and effectively estimate the length, position and distance of rebars.

This study proposes a method of construction component quality prediction on the basis of the MIV-BP neural network optimization. It uses MIV to display the change of the proportion of the input value in the neural network. By selecting the input value of the BP neural network model, it selects the variables that have a great impact on the model. In this way, the number of input parameters required for modeling can be reduced and the training accuracy of the model can be improved. Among them, MIV can carry out numerical transformation on an output parameter, and then predict through the BP neural network shape of the component after the change. By comparing the two prediction results before and after the change, the order of importance of each input parameter on the correlation of output parameters can be obtained, and the characteristic parameters that have less impact on the output results can be removed from the model, so as to screen the input parameters. It improves the complexity of BP neural network matrix when the number of input values increases significantly, and greatly builds up the calculation potency of the model. It helps to increase the reliable of the prediction of the production quality of PCC.

Covid-19 has caused a standstill in economies and social life worldwide, which is one of the most unexpected deadly events that remains a global pandemic to date. The construction industry is one of those negatively impacted by Covid-19; it brought unprecedented changes to a number of construction sites globally, which has raised serious concerns within the industry. Prior to the pandemic, the productivity of the construction industry has been unsatisfactory due to a variety of factors especially those related to the workforce. However, the advent of Covid-19 has exacerbated the industry’s vulnerabilities, threatening its overall performance and contribution to the economies of most developing countries. The pandemic’s impact on the construction industry have raised management’s pressure to achieve high productivity levels in most projects where labor productivity improvement is critical. The main objective of this paper is to explore the important factors that influence overall construction productivity in most developing countries, while considering the pandemic’s impact on the construction industry. To achieve that, an extensive literature review was conducted, and about 60 papers were shortlisted with the keywords ‘Covid-19’, ‘Construction Productivity’, ‘Construction industry’, ‘Developing countries’, ‘Construction labor productivity’ ‘Nigeria’, etc., among which studies conducted in most developing countries such as, India, Indonesia, Malaysia, Nigeria, Thailand, Uganda, etc., were focused on. Using Nigeria as an example, important factors applicable (but not limited) to Nigeria, were chosen and classified for further review and discussion. Finally, some critical factors were discussed and suggestions for mitigating the impacts they have on the industry were made. The findings and recommendations provided will contribute immensely in related study areas both for present and future research. It will aid researchers and policymakers in most developing countries in understanding the measures required for better management and decisions toward improving construction productivity in current and future construction projects.

Through growing built forms via pre-programmed rules and parameters, the idea of generative design is formed in this modern architecture, in which designers create form alternatives by adjusting input parameters. On the other hand, the emergence of computational platforms asks architects to put more attention to the liveability and sustainability issues of the built environment. While ex post evaluating design alternatives is feasible, it costs much time, crowds out valuable memory, and makes it hard for designers to arrive at optimality. By treating functions as observed variables, this study follows the “inverse problem” philosophy and proposes a novel inverse generative design workflow by finding the latent form variants that lead to these functions. It contains three steps: the first “Generation” randomly generates variants satisfying certain demands; the second “Selection” identifies a temporarily optimal layout given criteria; and the third “Augmentation” finds the major characteristics of this layout and uses it as new knowledge to augment the generator in the first step. This process will continue iteratively until one satisfying layout is found. With an urban renewal case study of To Kwa Wan, Hong Kong, this workflow demonstrates strong practicality by cooling the ambient outdoor environment by 0.10 °C and increasing 1.01% more sky views to the pedestrian.

Government and digital solution providers are important subjects involved in the digital transformation of construction enterprises. To explore the impact of government and digital solution providers on the digital transformation and development of construction enterprises, this study establishes a three-party evolutionary game model based on evolutionary game theory. At the same time, the system dynamics method is used to dynamically simulate the game process. The simulation results show that appropriate government subsidies and the establishment of a scientific and effective punishment mechanism can promote the digital transformation of construction enterprises. The impact of the collaborative income distribution coefficient on the strategic choice of construction enterprises and digital solution providers should be determined according to the actual situation, and the government only plays a regulatory role. Therefore, only by reasonably dividing the collaborative income distribution coefficient between the main bodies can we promote the digital transformation of construction enterprises to evolve into an ideal cooperation strategy.

In engineering construction, Indoor 3D laser scanning can detect the geometric quality of components (such as flatness and verticality of surfaces), which has a positive significance for geometric quality inspection. However, traditional indoor scanning schemes are often based on manual experience, and there are limitations, such as incomplete scanning long-time scanning. Therefore, this study proposed an automated LiDAR scan planning method for 3D indoor space at the same elevation using BIM and an improved genetic algorithm (GA). Required information, including geometric and semantic information as well as topology, is first processed and extracted accordingly. After extracting the region to be scanned, the GA is used to optimized the station positions considering scanning constraints (e.g., scanning visibility and scanned completeness). With an illustrated example, it is found that the proposed LiDAR scan planning for indoor 3D space can greatly save the time and labor cost, and solve the problems existing in the traditional indoor scanning schemes.

The ever-changing technical environment and growth of international construction projects present significant challenges for construction expatriates. These challenges demand a high level of competence in technical problem-solving and goal achievement to navigate the new working environment’s varying standards and practices effectively. This study aims to explore the capabilities necessary for construction expatriates to function effectively in a new technical context. To achieve this, we conducted one-on-one semi-structured interviews with participants to obtain their insights and perspectives on the factors required to succeed in this field. Our analysis revealed three critical facets of competency for construction expatriates: adaptability, knowledge, and motivation. The results of this study are novel and offer valuable insights for further research in international construction management. By understanding the capabilities required for success in the new technical context, construction expatriates can better equip themselves to meet the demands of the ever-changing construction industry. This study provides a foundation for future research and practical implications for those seeking to succeed as construction expatriates in an international context.

Digital twin construction (DTC), the virtual replica of physical construction, is an essential enabler for the promised Construction 4.0. Thanks to its real-time virtual-physical synchronicity, many uncertainty-tolerant functions, and applications, such as system integration, testing, monitoring, and maintenance, could be dynamically simulated and optimized through the DTC. The data foundation for creating or updating a DTC is real-time reality capture on construction sites. Despite a lot of efforts such as the Internet of Things (IoT) and AI cameras, so far, it is very challenging to capture the spatial-temporal information of the whole construction site. Time-dynamic 4D point cloud (4DPC) is an emerging sensing technology consisting of three-dimensional point cloud scans at a frequency, e.g., two frames per second. 4DPC has been successfully used in several industries, such as autonomous driving and motion analysis in sports science, due to its ability to capture dynamic objects and environments. This paper presents a strengths, weaknesses, opportunities, and threats (SWOT) analysis of the possible applications of 4DPC in DTC. In summary, 4DPC can effectively capture the dynamic feature of construction progress, with strengths in real-time information updating, covering more precise geometry dynamics, and providing localization and mapping information, etc., which are in line with the development of DTC. Meanwhile, the disadvantages include errors or noise in recognizing lign-permeable materials and water, enormous data volume, challenges in modeling spatial-temporal structure, and computation load in processing. The 4DPC receives several opportunities, such as establishing a dynamic data foundation for construction automation and modeling of workspace and integrating with 4D BIM for creating DTC; However, threats such as LiDAR-hostile weather and emerging competitors are also identified. Overall, 4DPC is a promising research direction that may interest DTC researchers and practitioners for various application situations in the construction industry.

Energy efficient retrofitting (EER) of existing residential buildings is one of the effective ways to achieve the carbon peaking and carbon neutrality goals in China. Despite the great efforts in the EEE filed of past years, the effectiveness of EER policies needs to be further improved. From the perspective of homeowners’ motivations for EER, this study aims to optimize EER policies. Based on a questionnaire survey in China, it was found that the motivations for homeowners’ housing EER are multifarious, mainly covering economic considerations, improving living quality, environmental preservation, technical aspects, social identification, and policy responses. On the basis of findings, it is suggested that EER promotion should not only highlight the environmental benefits of EER, but also emphasize the economic, social and technical benefits. Additionally, integrating EER into urban renewal and creating a positive EER atmosphere are also recommended. These findings shed light on EER promotion strategies.

Since the outbreak of the COVID-19 epidemic, the global economy has become increasingly sluggish, which has also affected the international engineering contracting market. With the normalization of the global epidemic, many China’s international engineering (CIE) contracting companies have been affected by the operation and basic work, and are facing various risks and crises. Therefore, the purpose of this study is to find out the critical risks and risk paths faced by CIE in the post-pandemic age. Through literature review and expert interviews, this study identified 20 risks. Then, the relationship between these 20 risks is determined by expert scoring. Using the network theory method to build a risk network, 9 key nodes are determined and divided into critical initial risks, critical transmission risks and critical endpoint risks. Finally, the risk transmission mechanism is obtained, and corresponding countermeasures are proposed for CIE contractors. This study is of great significance for the development of CIE in the post-pandemic age.

Based on the life cycle assessment (LCA), this paper divides the whole life cycle of prefabricated buildings into the production and processing stage, transportation stage, on-site building stage, operation stage, and abandonment stage, and constructs the calculation formulas of carbon emissions. An empirical study was conducted with a prefabricated building in Nantong. By comparing carbon emissions of different components and stages, the whole life cycle carbon emissions of the prefabricated building are analyzed. The results contribute to filling the gap of prefabricated buildings in the field of carbon emissions, for promoting the development of the low-carbon construction industry.

The COVID-19 pandemic has dramatically changed people’s lives and had a major impact on the field of research. In this study, bibliometrics was used to analyze 783 articles in the field of the post-COVID-19 era, among which 55 articles were the most popular management directions. Based on this background, carbon emissions and machine learning are selected for further analysis, which are the current hot research directions. There were only 22 and 12 related articles. This shows that management direction is a hot research topic in the post-COVID-19 era, while carbon emissions and machine learning are relatively few. This study sorted out and analyzed the specific research content of these 34 literatures. It is found that there are few literatures that apply machine learning to the study of carbon emission. But it also found that machine learning methods can be applied to carbon emissions even in the post-COVID-19 era. This not only provides scholars with new ideas and directions for their research, but also provides a theoretical basis for enterprises and governments to formulate corresponding carbon emission management policies.

The characteristics of modern construction projects, such as tight schedule, a large number of participants and complex working relationship, have significantly increased the job stress of project management personnel. However, there is no research on the change of job stress of construction project management personnel in the overall project implementation process globally. Based on literature review, Job-Demand-Control-Support (JDCS) classical stress theory and questionnaire survey, from the perspective of overall implementation process, this paper explores the changes of job stress levels of construction project management personnel in different stages of project implementation, and analyzes the differences of job stressors between the two types of management personnel of involving and non-involving in construction project overall implementation process. The results show that most of the management personnel experience the highest level of job stress in the tendering stage (36.1%), followed by the commencement stage (26.4%) and the normal construction stage (19.4%). There are significant differences in more than 50% of job stressors between the two types of management personnel. The management personnel involving in overall implementation process feel more strongly influenced by job demand stress, but they can better control their work and receive more support from their superiors and colleagues. The findings are valuable to understand the changing rules of job stress among management personnel involving in project overall implementation process, to develop a more effective job stress management program.

The coronavirus disease (COVID-19) has momentously affected the construction industry. The COVID-19 mitigation measures, in as much as they minimise the pandemic infections, likewise have disrupted the supply of materials, manufacturing, human resource availability, the workers’ close coordination, communications, and work execution. To this end, how the COVID-19 mitigation measures have affected the quality assurance (QA) of cross-border construction logistics and supply chain (Cb-CLSC) is not known clearly. This study addresses this knowledge gap. A conceptual analysis research method is adopted by engaging a desk literature review approach and Ishikawa diagram to identify and map the COVID-19 mitigation measures to their impacts on the QA of Cb-CLSC. The study identified COVID-19 mitigation measures and reaffirmed their negative and positive influences on the QA of Cb-CLSC toward developing an adequate QA system of Cb-CLSC for post-pandemic actions and to survive future pandemics. The COVID-19 mitigation measures include “lockdown”, “social distancing”, “travelling restrictions”, “workplace capacity Limit”, “use of personal protective equipment”, and “quarantine days”. Finally, an Ishikawa diagram-oriented conceptual framework is proposed by mapping the six COVID-19 mitigation measures to their potential impacts (positive and negative) on the QA of Cb-CLSC. The study makes a unique contribution to the body of knowledge by (1) evaluating the COVID-19 mitigation measures to illustrate how they have impacted the QA of Cb-CLSC and (2) proposing a conceptual framework based on the Ishikawa diagram that maps to understand how the COVID-19 mitigations measures have positively and negatively impacted the QA of CB-CLSC. The results of this study can assist practitioners in formulating a collaborative effort to embrace the pandemic for the post-pandemic era and successfully endure the risks of future pandemics.

To study the diffusion law of smoke and temperature at different fire locations in a subway station fire, the firing process under different scenarios is simulated by pyrosim. Taking Shixi station of Guangzhou Metro Line 10 as an example, a subway model is established to study the distribution and diffusion law of smoke when the fire source is located in the middle of the station hall, the side end of the station hall, the middle of the platform and the side end of the platform, and analyze whether the corresponding states meet the evacuation conditions of passengers. Evacuation shall follow the principle of 6 min, and the temperature at the height of 1.5 m from the ground on the evacuation route shall not exceed 60 ℃. The CO concentration shall not exceed 6.25 × 10−4 kg/m3. The results show that when the two factors of high-temperature gas flow and CO concentration are comprehensively considered, the most unfavourable position is ranked as follows according to the risk from high to low: middle of platform > middle of station hall > side of platform > side of station hall. Platform fire is less stable and more dangerous than station hall fire, and the impact of fire location on the smoke spread and personnel evacuation is closely related to building structure. The research results can provide a reference for fire fighting and escape strategies in subway station fires and can also provide a basis for the architectural design of subway stations.

From the life cycle perspective, service life of concrete is a crucial part to be evaluated but is normally overlooked in the environmental impact assessment. While recycled aggregate concrete is regarded as an alternative material to virgin aggregate concrete, CO2 Concrete has the enhanced mechanical and durability properties matching to those of virgin aggregate concrete beneficial from carbon conditioning treatment. To validate its potential on the environment reservation, this study attempts to conduct a comparative analysis of life cycle assessment (LCA) for virgin aggregate concrete and CO2 Concrete, with two functional units (FU) applied. When considering volume and compressive strength of concrete, CO2 Concrete is much preferred for the utilization to construction. However, involving the durability parameters in FU greatly affects the performance of CO2 Concrete. Results illustrate that the selection of FU is crucial to LCA study even though the same system boundary is applied, and the inclusion of durability parameters of concrete is essential to generate an unbiased LCA results. It is also suggested that various scenarios regarding different FU criteria are presented to make a more informed decision, since there is no standard for durability indicators of concrete to be included in the evaluation.

Construction firms are taking up both digital delivery and product platforms for industrialized construction to benefit from economies of scale and scope. The digitally-enabled product platforms becomes important, which such platform is defined as a collection of common and stable modules and interfaces that can derive products effectively using digital delivery. Existing construction management literatures have focused on the usage of product platforms; however, there is relatively less on platforming, which encompasses both the development and implementation of digitally-enabled product platform. This paper takes a comparative case study approach from nine international case firms to examine how construction firms strategize for platforming. Findings show that three typologies of platforms that firms developed a kit of parts only, and also developed structured interface, and also developed design rules. This paper articulates the influencing role of customer requirement certainties across multiple market segments in shaping these strategies. The contribution is to extend work on construction product platform strategies, by providing a novel classification of platforming strategies with a focus on digitally-enabled product platforms, under varied certainties of customer requirements across market segments. This has implications for practitioners and opens new areas for research, taking the characteristics of customer requirements within or across markets into account in strategic decision-making on digitally-enabled product platforms.

Modular construction has attracted wide attention from both academia and industry and has been viewed as a novel approach to improve construction sustainability. Nevertheless, few studies have focused on examining the embodied carbon (EC) emissions of modular buildings. This paper aims to assess the EC of a modular concrete high-rise residential building case by comparing it with a conventional prefabricated building case in Hong Kong and examining the EC reduction efficiencies of several low carbon concrete solutions. The EC during the cradle-to-site stage of both building cases was assessed using the process-based life cycle analysis method. A total of 10 scenarios of different low carbon concrete solutions were set, and their EC reductions were compared. The EC per construction floor area for an averaged floor of the modular building case was quantified as 532.1 kgCO2-eq/m2, higher than that of the conventional prefabricated building case (558.1 kgCO2-eq/m2). The largest EC reduction reached 23.8% in the modular building case, which was achieved by the combined use of rice husk ashes and the recycled concrete aggregates. Findings in this paper lay a good foundation for exploring novel low carbon concrete solutions for EC reduction of modular buildings in future research.

The quality of life (QoL) of the elderly has gradually become the focus of contemporary research. Elderly spent certain time staying at the community in their daily life, while studies have claimed the close relationships between built environment and the QoL of the elderly. With the advancement in the analytical tools, this paper aims to apply the machine learning approach to empirically examine the influence of the community environment on the QoL of the elderly. After extensive literature of relevant knowledge, a questionnaire survey was administered among the elderly. The collected quantitative data were subjected to a series of mathematical and statistical analysis analyses, and regression models for the relationship between community environment and the QoL of the elderly were established through support vector machine method. The results show that: 1) both the factors related the space and environment of the community can influence the QoL of the elderly; and 2) it was interesting to note that none of the facilities factor in the community imposes impact on their QoL. Practical recommendations are put forward according the research results in order to improve the community environment for the elderly, including building enough space, optimizing layout of monitoring equipment, maintaining ventilation to ensure air quality, and so on. This paper mainly contributes to apply the machine learning approach for examining the influence of community environment on the QoL of the elderly, which should enhance current body knowledge about the research related to the built environment for the elderly. The research findings should be helpful for the policy makers, facilities managers and academics to effectively improve existing practices regarding the management of community environment for better QoL of the elderly.

Reusability is a key aspect to achieving the transition from a linear economy with the “take-make-dispose” principle to a circular economy that follows the “reduce-reuse-recycle” principle. A literature review and expert interviews are conducted to identify the influencing factors of reusability and its assessment criteria. A distinction is made between three pre-conditional factors (Disassembly, Toxicity and Logistics) and six non-pre-conditional influencing factors (Data management, Standardization, Quality, Financial value, Over-dimensioning and Contracting). Expert panels are conducted to verify the conceptual model built on these factors and to determine the relative importance (weights). Subsequently, an assessment tool is developed to determine building products’ reusability potential in an early stage of the building process. The tool consists of three parts: a list of product properties, the input of assessment criteria, and a weighted average for calculating the potential. It takes three steps to determine the reusability potential. The assessment tool is tested and validated using a case study, where traditional products are compared to circular products.

Risk assessment is a key component of green buildings. In green building projects, the risk evaluation process is facing great uncertainties, like uncertain conditions, unreliable evaluation models, etc. Effective risk management depends on using the right risk evaluation models. Hence, this study aims to develop a novel risk structure matrix with probabilistic linguistic information for green building projects. To evaluate risks using probabilistic linguistic information, a risk structure matrix is firstly constructed. After that, the consensus reached by the group on the risk structure matrix has to be validated and confirmed. To demonstrate the effectiveness of the unique risk structure matrix, a case study was conducted. The research results have provided an alternative viewpoint for assessing risks, leading to improved risk management in green buildings.

Public is considered to be one of the most important participants in urban renewal projects. The mode of public decision-making has gradually changed from government-led to multi-subject consultative. A good mechanism of public participation can balance interests of all parties and improve decision-making effectiveness. Hence, it is of great practical significance to study public participation mechanism of urban renewal projects from the perspective of collaborative governance. Through previous analysis, it can be proved that the subject, process and environment of participation can affect the performance of urban renewal projects. Then theoretical model and hypothesis were put forward. Empirical analysis was conducted by Structural Equation Model. The results showed that 22 observation variables have passed confirmatory factor analysis, whose factor loadings are all greater than 0.6. The participation subject, participation process and participation environment had significant positive effects on performance of urban renewal projects, and the path coefficients are respectively 0.399, 0.288 and 0.275. On basis of model results and collaborative governance theory, the collaborative governance mechanism of urban renewal projects was designed from government level, public level and social organizations level. Finally, suggestions were put forward for each subject in order to realize effective public participation.

Institutional regulation is a crucial factor to improve megaproject social responsibility (MSR) and promote the sustainability of these megaprojects. However, the current stream of literature has rarely explored the impact of institutional regulation on MSR. This study examines how various factors of institutional regulation in megaprojects affect MSR behavior based on institutional theory. Using 217 survey data from China, the relationship between institutional regulation and MSR behavior was analyzed. The empirical results reveal that four types of government regulatory factors, namely institutional completeness, regulatory normality, institutional constraints, and policy incentives, significantly positive influence MSR behaviors, with institutional constraint having the most significant influence. It also finds that political connection positively moderates the influence of institutional regulation factors on MSR. Following new perspectives of MSR governance, these findings provide practical suggestions for both policymakers and managers on the implementation and governance of megaprojects.

With the renaissance of modular construction, quality control (QC) in the manufacturing stage warrants more attention as an important management process. However, many project managers are not familiar with the QC in the manufacturing stage, and the transportation limitation caused by the COVID-19 further increases the problem complexity. This paper aims at evaluating the performance of QC using popular QC tools in the manufacturing and discussing its effeteness on the process and management standardization. The evaluation and analysis were based on a real life modular construction project and an e-inspection application used in the manufacturing factory. The results showed that there were several out-of-control QC problems in the factory when the modules in progress and project complexity increased. More quality problems emerged after the 3D assembly of the module, especially during production works related to ceiling and wall installation. The results further proved the poor performance of the current QC in the factory and the importance of the process and management standardization. This paper pinpoints the importance of QC in modular construction manufacturing and its reference to improve the process and management standardization.

The promotion of green building materials has become a critical solution for most countries to realize low-carbon development, and this would involve multiple stakeholders. In order to formulate a more comprehensive promotion mechanism, this paper analyzed the factors affecting green material promotion from the perspective of multiple stakeholders. Firstly, a comprehensive literature review and the Delphi method of multiple stakeholders were adopted to determine the influencing factors. Then, the DEMATEL-ISM was employed to analyze the centrality, causality and hierarchical structure of the influencing factors. The results indicate that policy system is the cause factor with the highest causality degree, cost of adoption is the effect factor with the lowest causality degree, and initiative of enterprise is the influencing factor with the highest centrality degree. In the hierarchy structure, policy system is at the root level, initiative of enterprise and professional standard are at the deep level, maturity of industrial chain and technical feasibility are at the middle level, and social opinion, cost of adoption and performance of material are at the shallow level. Finally, three practical implications were proposed, including the government’s role to improve the policy system, the association’s need to formulate series of standards for industrial chain, the enterprise’s aspiration to increase investment in research and development. This study provides guidelines for China and other similar economies to formulate promotion mechanisms of green building material promotion and adoption.

Pursuing more sustainable construction projects has become a global priority. The construction industry is responsible for the massive use of freshwater resources and fossil fuels and several other environmental impacts, in addition to considerably affecting the gross domestic product (GDP) worldwide. In this vein, it is crucial to find strategies to develop a sustainable built environment based on a triple-bottom-line (TBL) strategy, concurrently considering environmental, social, and economic factors. The application of BIM-based Digital Twins seems to offer a tenable solution for overcoming the challenges related to achieving sustainability in the construction and real estate sectors. This concept is associated with developing a digital counterpart of the facility to assist the decision-making process throughout its life cycle, using real-time data and an actual connection between the 3D digital model and the physical asset. A BIM-based Digital Twin can be advantageous for a single building or an entire city and is, therefore, often related to the development of smart cities. This study’s novelty is presenting a structured literature review that defines the most recent developments in BIM-based Digital Twin applications for the real estate and construction sectors regarding sustainability goals. Based on this literature review, the authors present a discussion of how the knowledge acquired so far can be diffused into the built environment.

The competency of project managers has critical influences on the success of construction projects. However, the research on the importance of competency factors and the actual competence level of project managers in the Chinese construction industry remain rare. On the basis of literature review, this paper develops the professional competency index system of Chinese construction project managers, which includes 3 dimensions and 7 indicators. Based on a questionnaire survey, descriptive statistical analysis and importance-performance analysis (IPA) were conducted to explore the difference between the importance of professional competency factors and the actual level of their competencies, to understand the weak areas of project manager’s competency and to further improve their professional competencies. The results show that the actual level of all professional competency indicators is lower than their corresponding importance level, indicating that in general, the professional competency of project managers in the Chinese construction industry needs to be comprehensively improved. Among them, the largest gap measured in mean value differences is the “knowledge” dimension, and in terms of factors the gaps for “professional knowledge”, “practical ability” and “career planning ability” are the most significant between the perceived importance and actual levels. These are the aspects that project managers need to focus on improving. The research results in this paper provide theoretical reference for further improvement of the competences of project managers in the Chinese construction industry.

As an important transportation hub in the development of the city, the regional development pattern of the airport is an important indicator of the city’s economy. The formation of corridor development zone between the airport and the city will gradually become the new development area of the city’s economy. The development of the airport has resulted in traffic congestion in the surrounding area, residential noise, and land use restrictions. This paper will explain how to effectively deploy the contradiction between airport development and urban development from two aspects of noise and airport operation efficiency and demonstrate it through practical cases.

In this paper, the causes of super high-rise safety accidents are analyzed based on the Bivariate Probit model with 185 typical accident cases in recent years. The accident information is preprocessed and classified from three dimensions, i.e. accident characteristics, project characteristics and organizational characteristics. The empirical results indicate that there is a positive correlation between the fatal accidents and the economic loss accidents, and the 12 selected variables such as accident type, accident hazard source, accident occurrence stage, building purpose, project area, and whether the company has construction experience in super high-rise buildings, have significant effects on the occurrence of the two types of accidents.

The “double carbon” target reflects the new goals and requirements of China entering a new stage of development. In order to achieve the goal of Chongqing’s carbon peak, it is a timely and urgent problem to analyze the carbon emission level of Chongqing’s transportation industry. Based on the data on per capita GDP, passenger transport turnover, freight transport turnover, energy intensity, urbanization rate, private car ownership, and energy structure of Chongqing from 2000 to 2020, this paper applies the extended SRTIRPAT model to predict the carbon emissions of Chongqing’s transportation industry from 2021 to 2035 with different scenarios. Finally, this paper puts forward some appropriate suggestions based on the prediction results.

With the advent of the post-epidemic era of COVID-19, the whole life-cycle of hospital building construction and management has become one of the engineering community’s top priorities. This is a time when there is a great need for more advanced technology to oversee and update the construction of hospital buildings. The BIM+VR model can significantly improve hospital management efficiency and uncover new management opportunities. In this paper, the possibility of using BIM+VR in each phase of the project was scored and elaborated on by experts, and in a particular hospital construction project, the application and optimization of the model are proven. Finally, the whole life-cycle application potential of BIM+VR is predicted. The findings reveal that the hospital building model generated using the parametric modeling technique may be swiftly updated, restructured, and optimized utilizing a virtual reality environment. When project issues arise, the BIM+VR model can also provide rapid feedback. In certain instances, repetitive modeling is avoided by combining BIM and VR models, which also improves project efficiency and controllability. The combination of the BIM+VR model offers some relief from repeated modeling, enhances project efficiency and controllability, and enriches project management technology approaches by presenting opportunities for improvement in the creation of the whole life-cycle management of hospital buildings.

Labor shortage and low labor productivity are critical issues in the construction industry. Robots, as powerful technologies to improve the productivity in digitization era, are being used to assist in addressing those challenges. Particularly in recent years, interest in construction robot and related research has risen remarkably. To gain a deeper understanding of this burgeoning research field, the study provides a bibliometric analysis of 473 related papers retrieved from WoS (Web of Science). Performance analysis and science mapping were used to identify the research trend, analyze the relationship among authors, identify the top publication sources, analyze the region activity, as well as knowledge base and dominant research sub-field. The results indicated that the number of papers focusing on construction robot has been continuously growing after 2013. ‘Automation’, ‘system’ and ‘design’ are the most addressed topics in construction robotics. The study therefore would be valuable for providing a better perspective for practitioners and researchers to understand the development of construction robots and facilitating the building of intellectual wealth of robots in the construction industry.

The objective of this research is to present a method for developing the domain ontology of construction contracts. Ontology is a promising tool to organize domain knowledge and improve knowledge sharing. In this article, we propose a mixed contract ontology development approach. We first define the root classes by expert knowledge; then collect various concepts to build the subclasses and their interrelations. Finally, we transfer the contract ontology into a knowledge graph based on the results to test the ability of the ontology in implementation. The method provides a reference for the creation of the construction contract ontologies and serves as a fundamental support for the development of construction contract knowledge graphs.

Green building evaluation systems are certification tools developed to enhance building performance and reduce carbon emission. To meet the increasingly aggressive “carbon neutrality” and “carbon peaking” targets set by different countries, it is commonly believed that it is vital to identify the critical carbon emission reduction indicators from the green building evaluation systems and assess their impact on carbon emission. The paper thus aims to identify the common carbon emission reduction indicators of green building evaluation systems and to ascertain the supplementary carbon emission reduction indicators of different countries to achieve the “carbon neutrality” and “carbon peaking” targets using a meta-analysis approach. Firstly, this paper refined 11 articles regarding the impact of carbon emission indicators in the green building evaluation systems on the emission reduction effect. Secondly, the paper compared and analyzed the carbon emission reduction indicators in different green building evaluation systems such as LEED, BREEAM, Green Star, and CASBEE. Thirdly, the paper identified the common carbon emission indicators, including energy, transport, materials and resources, pollution and waste, and regional optimization. Comparing the green building evaluation systems can ascertain the emission reduction indicators that need to be supplemented to achieve the “carbon neutrality” and “carbon peaking” targets in different countries. The research findings provide references for improving the green building evaluation systems and help enhance the realization of carbon neutrality and carbon peak targets worldwide.

Due to the resource-intensive nature of infrastructure and buildings, the construction industry consumes a lot of resources, which have significant adverse effects on the environment, economy, and society. The choice of construction materials has a critical impact on a building's operating energy usage, related CO2 emissions, and indoor air quality. It is commonly acknowledged that green construction materials (GCMs) could reduce the adverse effects while improving buildings’ performance. To encourage GCMs, distinct organizations and regions have established different green construction material certification (GCMC) systems. This study aimed to analyze the similarities and discrepancies in requirement categories and detailed requirements for specific materials among various GCMC systems. The findings show that resources, health, energy, and environment are shared interests of Cradle to Cradle Certified (C2CC), Mainland China, Hong Kong, India, and Singapore GCMC systems. C2CC highly values social justice. Both Mainland China and India GCMC particularly take into account the effectiveness or quality of the products. The grades for goods or materials in various GCMC systems are very variable. The requirements for ready-mixed concrete in Mainland China, Hong Kong, and India GCMC differ significantly. Hong Kong GCMC only considers the carbon footprint for ready-mixed concrete, while the other two systems focus on several attributes. The GCMC in India, as opposed to those in Hong Kong and Mainland China, has given credit points to the benchmark for each criterion. The findings can help regional or organizational decision-makers better comprehend the variations across different GCMC systems and enhance their GCMC system.

Computer vision has been widely used in construction sites for progress monitoring, and safety monitoring. However, collecting data from construction sites and labeling them into datasets is a time-consuming, labor-intensive, and costly task. Therefore, a synthetic dataset generation approach for construction site entities based on the game engine is proposed to solve the problem of the lack of construction site datasets. In this research, construction site scene models are formulated by grouping existing digital on-site assets, and image annotation and camera calibration files are automatically generated by developed scripts in the selected game engine. The movement of the model is also controlled by developed scripts and the scene is rendered using High-Definition Rendering Pipeline (HDRP) to obtain high-resolution images. Components such as transform and Box Collider are used to get the coordinates of the object relative to the camera and the size of the bounding box, and to automatically generate the labels. In addition, the focal length, field of view (FOV), and other parameters of the camera component are utilized to calculate the camera Intrinsic when generating calibration files. By this method, a large amount of synthetic data can be quickly acquired and labeled, significantly reducing the time of dataset generation of on-site entities. Finally, the computer vision model trained on the synthetic dataset achieved 91.6% mAP on the real dataset.

Construction projects are vulnerable to sudden crises such as the COVID-19 pandemic. Hence, many construction companies are increasingly realizing the importance of project resilience. However, most of the current studies have remained on the organizational resilience level, and there is a lack of research on project resilience. This study develops a theoretical model to explore the relationships among inter-organizational activities, organizational crisis awareness, organizational crisis response, and project resilience in the context of COVID-19 pandemic; and empirically tests the proposed model using data collected from 98 projects during the pandemic. Results show that inter-organizational activity is an important factor affecting project resilience. Meanwhile, organizational crisis awareness and organizational crisis response play important mediating roles. This paper reveals the significance of inter-organizational activities in improving project resilience and provides a new perspective for further exploring project resilience. It also helps construction enterprises formulate additional appropriate strategies to deal with sudden crises in the post-pandemic era.

The complicated and uncertain character of megaprojects requires appropriate analysis of affected communities to achieve project objectives and accommodate megaprojects-induced internal displacement participation mechanism. Although previous scholarly works have contributed to the development of public participation theory, these theories have not been fully acknowledged from the affected community's perspective in practices, especially in megaprojects-induced internal displacement. In this study, an extensive literature review was conducted to get a general knowledge of the relationship between the willingness to participate and other prior key determinants. While no cohesive national governance strategy for megaprojects-induced internal displacement has been developed, the lack of recognition for the issue at the governmental level contributes to internal displacement risk creation, placing vulnerable communities at little or no influence on the process. Therefore, this study proposes a conceptual model of megaprojects-induced internal displacement that can be used to diagnose and assess willingness in community participatory research and practice. The findings will help academics and decision-makers be concerned about the key participation determinants to avoid ambiguity and disparity in understanding the adoption progress and status of community participation.

Condition assessment and health monitoring (CAHM) of buildings require effective and continuous detection of any changes in the material and geometric properties of components to detect defects in time. However, traditional manual-based detection methods are inefficient and error-prone. Smartphone/tablet-based detection has achieved real-time detection of the CAHM with improved efficiency, however inspectors still need to hold the smart devices in hands, resulting in inconveniency and uncomfortable working experience. In this study, a head mounted display (HMD)-based collaborative method for real-time detection and tracking of defects (i.e., crack, swell, peel, seepage, and mould) in building was developed by combining an object detection algorithm you only look once version 5 (YOLOv5) with multi-object tracking algorithm Deepsort. According to the analysis of the experimental results, the developed method is promising and efficient to detect and track various types of building defects.

High-rise high-density cities around the world suffer from severe urban heat island effects. Greenery has the potential to heal urban microclimates, such as shading, lowered air temperatures, and increased humidity, apart from other benefits to urban health. Existing numerical simulation studies employing simplified, proxy greenery models have validated the potential at a macro level; However, the human-centric three-dimensional nature of greenery (e.g., tree crown volume, canopy density, and leaf area index) was ignored, leading to inaccurate results for buildings and blocks, especially in the high-rise high-density settings. This research proposes a precise voxel modeling of urban green volume for building and environment simulations. First, two scans, i.e., the airborne scan of tree canopies and hand-held LiDAR scan of lower parts, are registered and merged into a voxel model at 0.5-m resolution. Then, simulations and analysis of the voxelized green volume are conducted using Rhinoceros and Grasshopper. A preliminary experiment of shading on a university campus in Hong Kong proved the concept of this study. Future work will be directed to analysis of the voxel resolution and more types of simulations such as winds and urban thermal exposure.

Conflicts caused by mega projects (MPs) always generate strong public opposition and pose significant threats to social harmony. Combined with ubiquitous online access, the spread of online rumors is unparalleled. However, rumor rebuttal on social media has been largely overlooked in engineering sociology. This study attempts a cross-disciplinary approach by crawling data related to anti-rumors of a representative MP from Sina Weibo to identify anti-rumor topics and online stakeholders, and investigates the effectiveness of anti-rumor strategies. The results suggest that the sentiment-based echo chamber effect is not significantly present in both participant and strategy networks. Anti-rumor messages of traditional media and elites are effective, while that of self-media are mainly ineffective. Meanwhile, anti-rumor strategies have different effectiveness in three frameworks. Refutation and guide strategies are effective in the assessment and risk perception framework, sarcastic and disbelief strategies are counterproductive in the risk perception and progress framework, while interrogatory strategy has opposite effects in the assessment and risk perception framework. This research can contribute to developing a systematic understanding of anti-rumor communication and provide recommendations for authorities to intervene social conflicts caused by MPs.

As a major public health event, the impact of the COVID-19 outbreak was significant. Based on data from December 2021 to March 2022 for the medium to high-risk areas reported in Guangdong Province and their corresponding street communities, the paper studied the spatial characteristics of the epidemic and the gathering distribution of points of interest (POI) and traffic points, and the association between them, which is analyzed by kernel density analysis, cold-hot spot analysis, and spatial autocorrelation analysis. It was found that the spatial distribution of the epidemic in Guangdong Province had significant positive spatial clustering characteristics, and the spatial clustering pattern of the epidemic was roughly the same as the clustering distribution pattern of POIs and traffic points.

River systems worldwide are severely degraded due to diverse threats. River restoration aiming to reverse negative impacts has become a priority strategy for enhancing social benefits and river ecosystems in urban development. River restoration projects provide multiple ecosystem services to human beings, and social benefits derived from cultural services have been underestimated and have attracted increasing interest from researchers and policymakers. The purpose of this research is to investigate the public evaluation of the impacts of river restoration projects on social benefits. Two restored river sites in Hong Kong were selected as the case sites. The questionnaire survey was implemented to collect the public evaluation of the social benefits derived from the cultural services of the restored projects, and their visiting patterns and feelings. Regarding the evaluation of the cultural services, the public thought the restored rivers contribute to public health, quality of life, and recreation activities. Additionally, the respondents thought there is a substantial improvement in the aesthetics of the environment. Many people are supportive of similar projects in future implementation. The results summarized from the 158 responses show that local citizens frequently visit the restored river sites for diverse motivations. People generally feel good about air condition and sounds at the restored sites. Most people are in a good mood when staying at the restored sites, indicating that river restoration contributes to the public’s healthy living. A large proportion of people think there are no negative impacts of projects though some people are concerned about insects, guano, and animal disease. Generally, the public holds a positive view of the projects. The research finding enables policymakers to understand the public’s opinions on the river restoration projects and increase their confidence in implementing more similar projects in the urban environment.

Versioning in Building Information Modeling (BIM) is essential for design collaboration. However, current version control systems risk data manipulation because they rely on centralized versioning architecture, which might result in rewriting, losing design traceability, and causing arguments. Blockchain technology offers a decentralized, immutable, and traceable database model, making it a possible solution for secure version management. Therefore, this study presents a blockchain-based framework with two key contributions. First, a distributed versioning environment is established, leveraging blockchain and common data environment (CDE). Second, a smart contract cluster (SCC) is developed to automate versioning operations in the blockchain. The proposed framework is evaluated and validated in design scenarios based on an actual project. Results show that the blockchain is a promising solution for efficient and secure BIM versioning.

The increasing complexity of engineering projects is a result of multiple stakeholder interfaces, interdependent processes of different tasks, and large amounts of information generated and flowing within project networks. To cope with complexity and deliver quality projects, the current literature argues that integration is needed in the mindsets and skills of both project managers and project teams. However, how to facilitate integration through project management, especially in complex environments, has yet to be explored with empirical evidence. We studied an ongoing construction project with highly complex characteristics and examined the transition from fragmented management practices to an integrated approach and how it helped improve the project team’s ability to manage project complexity. The results of the case study reveal the elements of integration management and its dynamic nature, where routine development and boundary spanning are critical to its effectiveness.

Construction is considered one of the world’s oldest industries. On the other hand, the emergence of new technologies has led to a significant change in job popularity among the younger generation. With COVID-19 affecting the world, many new phenomena were triggered, including the job image of the Construction Industry, causing direct consequences on the sector, such as the reduction of available labour. This study analyzes how social media data affected the construction industry’s image during the pandemic. Comments were collected on Twitter for 3 years from 2019 to 2021, followed by NLP (Natural Language Processing) methods to process the data through sentiment analysis. Specifically, this research provides insight into what motivates the younger generation to join the construction industry. The outcome can also assist construction companies in improving their incentives among the suggested dimensions and sectors to enhance the recruiting rates among young job seekers. In addition, it also provides a deep understanding of how the pandemic changed the generation’s perception of such a traditional sector. The study discovered the topics that were most frequently discussed during the peak of the pandemic, as well as how they affected construction companies. Construction jobs and work environment can be designated as one of the peak topics during this time, as well as Leadership and Management, implying that they may be a leading cause of employee turnover.The findings can directly help company behavioral management decision-makers develop and evaluate initiatives to improve construction companies’ job image.

With the successful landing of blockchain in more and more industries, the construction industry is on a path to finding possible application scenarios of blockchain. Blockchain, described as “open and transparent”, “safe and trustworthy”, and “immutable and traceable”, is expected to break the barriers to trust in the construction supply chain (CSC). However, the application of any new technology will generate more unknown risks due to the increase of uncertainties. As a result, the objective of this paper is to find the critical risks associated with blockchain adoption in China’s CSC. Through a literature review, this research identified 26 risks of blockchain application in the CSC, including seven types of risks: technical risk, industry risk, social risk, cost risk, management risk, legal risk, and force majeure risk. Then, a risk network was set up to analyze the importance of risk nodes by considering risk associations. Results showed that “Technical suitability”, “Public attitude”, and “Information asymmetry” were perceived to be the critical risks. This research had great reference significance to the future development of blockchain applications in China’s CSC.

Sustainable developments have been one of the main social forces worldwide, especially in the building sector. As the current biggest energy consumption industry of 35%, it is urgent to solve severe energy issues through advanced energy-saving technologies. Energy consumption prediction, as one of the important building energy management tools, can evaluate energy conservation policies and services timely. Unfortunately, there is still a significant difference between actual and predicted values. A consensus about the life cycle energy boundaries for buildings is being challenged. Some studies believed that the mobile energy related to building location can be accounted, except for traditional embodied and operational energies. Besides, deep learning was regarded as a method better than other simulation models in time series forecasts. To fill the gap between actual and predicted energy consumption values, this paper proposes to extend the life cycle energy boundaries of buildings and choose the Long Short-term memory model (LSTM)) to predict the building energy consumption in China from 2020 to 2029, which is based on the historical data collected from 2005 to 2019. Results show that there was a remarkable increase in the past 15 years for the total life cycle energy consumption of buildings, but afterwards it will fluctuate at around 1,050 Mtce because of potential influencing factors such as recyclable concrete and prefabricated process applied into an increasing number of newly built buildings. Mobile energy consumption accounted for 24% share of total energy consumption, but it is expected to fall significantly in the next decade. Overall, this study provides a pathway to help reduce building energy consumption prediction errors.

The growing global concern about climate change due to greenhouse gas emissions from vehicles coupled with the depletion of natural resources is driving the adoption of alternative fuel technologies in the global economy. Electric vehicles are positioned as a green, clean alternative technology with the potential to enable an efficient transition to sustainable low-carbon emission transport systems and to conserve natural resources. New energy vehicles represented by electric vehicles have less pollution, low noise and high energy efficiency, which is the direction of future automobile development. Despite the announcement of preferential policy measures to encourage the adoption of electric vehicles, multiple potential barriers that interact with each other have hindered the penetration of electric vehicles in some countries. While researchers have identified some restricting factors in European and American countries, the question is “There is a lack of empirical research on the restricting factors of EV promotion in the Chinese context, and how do they interact with each other?” A two-phased AHP-FCE tools are applied. Firstly, Analytic Hierarchy Process (AHP) method is used in ranking and prioritizing the important restricting factors/sub-factors. Then, multi-level Fuzzy Comprehensive Evaluation (FCE) method is applied to obtain the final evaluation score. Our research contributes to a better understanding of the multifaceted nature of EV restricting factors and their interdependencies in policy and decision making. This paper provides a valuable reference for the construction of low-carbon cities and urban sustainable development.

In China, construction and demolition (C&D) waste is one of the largest waste streams. Majority of the waste is improperly managed and disposed in dumping sites. Therefore, there is a pressing need to properly manage C&D waste for a sustainable future. Legislative measures could be adopted to stimulate C&D waste recycling. It is found that increase in landfill charge could decrease reliance on landfill. This study develops a system dynamics model to evaluate impacts of landfill charge and its combination policy with development of recycling centres on recycling rate of C&D waste. The model is developed through Vensim PLE software, and urban area of Chongqing, China is selected as a case study. Findings indicate that landfill charges of 10 yuan/ton (or US $1.47/ton) and 30 yuan/ton (or US $4.42/ton) have limited impacts on recycling rate. Landfill charge of 50 yuan/ton (or US $7.36/ton) can make some differences. Impacts of combination policies are obvious and recycling rate in this scenario is higher than that in base scenario. Findings of this paper could help improve the efficiency of C&D waste management and adopt market for recycled aggregate.

Workers unsafe behavior, particularly of those of young workers, is a main cause of accidents in the construction industry, partly because of their rebellious psychology. How young workers’ rebellious psychology affects unsafe behavior remains unknown. This paper explains the phenomenon and answers this question from the perspectives of equity theory and social psychology theory, and puts forward intervention measures according to the internal and external factors. The measures include understanding young workers’ needs, improve organizational fairness, providing safety training and learning; implementing technology-enabled psychological measures and creating a strong safety culture. Finally, the paper discusses future research directions which includes adapting new research methods for data collection and analysis by developing and applying neuro-technology enabled experimental design.

This study explores the Socio-economic drivers of energy consumption in three Urban Agglomerations and cities in the Yangtze River Economic Belt during the 2011–2020 period. Using the logarithmic mean Divisia index (LMDI) method decompose the change in energy consumption into five factors: energy intensity effect ( $$\varDelta C_{EI}^{{}}$$ Δ C EI ), per urban population GDP effect ( $$\varDelta C_{PG}^{{}}$$ Δ C PG ), urbanization rate effect ( $$\varDelta C_{UR}^{{}}$$ Δ C UR ), investment population support coefficient effect ( $$\varDelta C_{IP}^{{}}$$ Δ C IP ) and investment effect ( $$\varDelta C_{I}^{{}}$$ Δ C I ). The main results showed the following: (1) $$\varDelta C_{I}^{{}}$$ Δ C I ranks the first most important factor in three Urban Agglomerations and in whole cities from 2011–2020 period; (2) The impact of $$\varDelta C_{UR}^{{}} $$ Δ C UR on the reduction of energy consumption is negative in three Urban Agglomerations and in whole cities. (3) $$\varDelta C_{EI}^{{}}$$ Δ C EI has a strong impact on swelling energy consumption in three Urban Agglomerations and in 68 cities during the study period. The most of the cities mainly focusing on Urban Agglomeration in the middle reaches of the Yangtze River as well as Yangtze River Delta Urban Agglomeration. (4) $$\varDelta C_{IP}^{{}}$$ Δ C IP has a most powerful force to reduce energy consumption in three Urban Agglomerations and in whole cities over the whole study period. (5) The role of $$\varDelta C_{PG}^{{}}$$ Δ C PG in the increase of energy consumption cannot be disregarded in three Urban Agglomerations as well as in cities particularly showing in Shanghai, Nanjing. Therefore, decision-makers should balance the social and economic implications of energy usage in addition to developing suitable fixed asset investment strategies.

Urban spatial structure and its spatial evolution is of great significance for optimizing urban spatial structure and formulating urban spatial planning. Numerous studies have showed that many megacities in the world are moving towards polycentric urban spatial structure, but few studies have considered the role of urban polycentric planning in this process. Based on mobile phone signaling data of Shenzhen in 2012 and 2016, this paper explored the evolutionary characteristics of employment spatial structure in Shenzhen and evaluated the role of urban polycentric planning in the process. Firstly, this study found that Shenzhen is also moving towards a polycentric urban spatial structure and the differences among employment centers are shrinking. In 2012, six employment centers were identified and mainly distributed in Futian District and Luohu District, indicating that the monocentric spatial structure of Shenzhen remained prominent until 2012. By 2016, this study identified ten new employment centers. Except for Futian District and Luohu District, Nanshan District, Longhua District and Longgang District have emerged new employment centers. Secondly, this study confirmed that the polycentric development is a trend in the evolution of Shenzhen’s employment spatial structure, but employment centers identified in 2012 and 2016 have a relatively limited influence on the employment population distribution, especially employment centers outside the Futian District, Luohu District and Nanshan District. Finally, this paper found that the urban master plan has played a significant guiding role in the formation of employment centers in the central city and the central region of Shenzhen, while the planned subcenters, located in the eastern and northwestern of Shenzhen, have not yet been formed and still need to be cultivated and further guided.