With updated equipment and maturing technology, the applications of augmented and virtual reality (AR/VR) technologies in the architecture, engineering, and construction (AEC) industry are receiving increasing attention rapidly. Especially in education and training, an increasing number of researchers have started to implement AR/VR technologies to provide students or trainees with a visual, immersive, and interactive environment. In this article, a systematic review of AR/VR technologies for education and training in the AEC industry is conducted. First of all, through comprehensive analysis, 82 related studies are identified from two databases, namely Scopus and Web of Science. Secondly, the VOSviewer is used to analyze the current status of AR/VR for education and training in the AEC industry. Thirdly, the identified studies are classified into different categories according to their application domains by qualitative analysis. Fourthly, after a further filtering, 17 out of the 82 studies are included in the meta-analysis to quantify the actual impact of AR/VR. The results indicate that there are some limitations in the applications of AR/VR for education and training in the AEC industry. Finally, to further explore the reasons for the existence of limitations, the 82 studies are summarized to analyze the current challenges of AR/VR for education and training in the AEC industry. This study also provides insights into future trends in AR/VR for education and training in the AEC industry.

The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

With the rapid development of the economy, people are paying more and more attention to the environmental problems. In this circumstance, the concept of a circular economy is proposed for making efficient use of resources and minimizing the production of waste and other emissions. Each year, the construction sector consumes a vast volume of resources and makes impacts on the environment. To align with the development of the circular economy, the concept of green building is proposed. In China, though the concept of green building has been promoted for decades, the development status is far from optimistic. Thus, this paper aims to investigate the barriers that hinder green building development (GBD) in China. Through a systematic review and semi-structured interviews with experienced industrial practitioners, 24 potential barriers of GBD in China were identified. A questionnaire survey was then conducted for data collection. After descriptive and inferential statistical analyses, a partial least squares structural equation model (PLS-SEM) was established to investigate the impacts of different barriers on GBD. Results showed that the lack of policy and industry guidance, the immature market environment, and the lack of environmental awareness are the most important GBD barriers in China. This research can assist stakeholders in better understanding the status of GBD in China and enable decision-makers to formulate appropriate strategies to promote green building.

Public participation is an important procedure of the environmental impact assessment. Effective public participation is essential to the Public–Private Partnership (PPP) projects as such projects usually exert tremendous impacts on the environment and society. However, in literature, there are few studies investigating the driving factors of public participation in PPP projects, especially in the context of China. To bridge this research gap, this study proposed a theoretical model, which incorporates contextual factors (i.e., perceived benefit and perceived risk) into the classical Theory of Planned Behavior model, to explore the determinants. The initial proposed model was tested using structural equation modeling. Analysis results indicated that attitude towards behavior, subjective norm, perceived risk and perceived behavioral control were the four significant driving factors of public participation in PPP projects, whereas perceived benefit had limited impact. Furthermore, this study evaluated eight public participation approaches in PPP projects. Results revealed that the public were more willing to participate in public decisions through the internet platform, followed by the information disclosure or consultation provided by the government. The research findings derived in this study can provide valuable reference for the government to promulgate proper policies to attract more public participation in PPP projects. Moreover, the research idea and methods used in this study can be popularized in other countries to enhance the public participation in PPP projects.

Prefabricated steel box girders (SBGs) are widely adopted in bridge engineering due to their light weight and low lifecycle cost. To smoothly assemble SBG components on a construction site, it is necessary to inspect their geometric quality and ensure that all the as-is SBG components have the correct dimensions. However, the traditional inspection method is time-consuming and error-prone. This study developed a non-contact geometric quality assessment technique based on 3D laser scanning to accurately assess the locations and dimensions of SBG components. First, a robust normal-based region-growing algorithm was developed to divide the SBG components into segments with different labels. The scanned data related to the T ribs were then extracted through the proposed subtraction algorithm after the identification of the steel cabin. Lastly, the required items for geometric quality inspection were calculated based on the extracted as-is SBG components. The feasibility of the proposed geometric quality assessment method was validated through a real SBG project. Field test results showed that the developed inspection technique could assess the geometric quality of prefabricated SBG components in a more accurate and efficient manner compared to traditional measurement approaches.

Wood is generally considered as a sustainable construction material. However, there are not sufficient wood resources in many countries or regions, especially those short of land resources. These countries and regions have to import wood from overseas. Therefore, it is imperative to determine how to choose sustainable importing sources in order to improve the sustainability performance of using wood in construction. This study compares the sustainability performance of wood imported from different regions by considering wood harvesting, manufacture, and transportation. A framework accounting energy consumption and CO₂ emissions is developed for sustainability assessment. The results show that importing wood from Canada, Australia, and New Zealand to Taiwan demands a relatively lower amount of energy than from other regions. Specifically, importing wood from Canada (West) demands the lowest amount of energy (2095 MJ/m³), while importing wood form Brazil consumes the highest amount of energy (5356 MJ/m³). In addition, findings showed that the CO₂ emissions generated from importing wood from Sweden are significant lower than those from other regions, although the energy consumed during the importing process is relatively high. The study also revealed that the wood manufacturing process and marine transportation contribute to the most energy consumption and CO₂ emissions among all importing processes analysed from most of studied regions.