Khoo Terh Jing

Although 5D Building Information Modeling (5D BIM) offers potential for cost control, its implementation remains constrained by multiple barriers. This study examines the relative perceived importance of six factors associated with 5D BIM implementation and their direct associations with project cost performance among higher-tier general contractors in Guangdong Province, China. Survey data from 234 contractor organizations, each represented by a knowledgeable key informant, were analyzed using SPSS, PLS-SEM, and IPMA to rank the factors, estimate their direct associations, and identify managerial priorities. Environmental factors received the highest perceived-importance ranking. The structural model shows significant positive direct associations between project cost performance and four factors: technological, organizational, environmental, and government policy; government policy has the largest standardized path coefficient among the six predictors tested. Operational and project-related factors show no statistically significant direct associations. IPMA places organizational and environmental factors in the high-importance, high-performance area, whereas government policy has high importance but below-average performance. This first empirical test of the previously developed extended TOE framework distinguishes perceived implementation importance from direct cost–performance relevance and clarifies its theoretical boundary. These context-specific findings inform contractor management and policy implementation and support future validation across regions, contractor types, and stakeholder groups.

Digital twin (DT) technology is recognized for its transformative potential to enhance efficiency in the construction process. However, the full potential of DT in construction practices remains largely unrealised. Moreover, few studies explore how DT success factors affect efficiency improvement (EI), energy optimization (EO), and cost control (CC) in the context of economic strength (ES). The study applied a hybrid research method to examine the impact of key DT success factors on EI, EO, and CC under the moderation of ES. After a critical literature review, five key DT success factors were identified. Then, 490 valid questionnaires were analyzed with the Partial Least Squares Structural Equation Model (PLS-SEM) to assess how success factors affect DT effectiveness. This is complemented using extreme gradient boosting (XGBoost) to assess prediction accuracy and understand which factors most influenced EI, EO, and CC. Research shows that ES exerts a significant positive influence on the relationships between most success factors and performance outcomes. High levels of ES enhance the contribution of success factors to performance in EI, EO, and CC. Resource management (RM) has a strong influence on EI and EO, but a weaker influence on CC; process optimization (PO) has the strongest influence on EO, a moderate influence on CC, and the weakest influence on EI; real-time monitoring (R-Tm) primarily affects EI; sustainable design (SD) has a comprehensive and significant regulatory effect on EI, EO, and CC; and predictive maintenance (PM) has a strong influence on both EI and CC. In practice, it offers practical guidance for implementing DT and supports policy and resource planning for building stakeholders.

6D building information modeling (6D BIM) has been proposed as a sustainability dimension in BIM technology. It serves as a life-cycle sustainability assessment tool and an important method for decision makers and designers to make informed decisions in sustainable design and facility management. However, there is currently a lack of comprehensive evaluation of the key influencing factors of 6D BIM in residential Building-Integrated Photovoltaic (BIPV) systems. Previous studies have typically examined BIPV performance or BIM-based sustainability workflows in isolation, lacking a comprehensive analysis of how 6D BIM enhances BIPV sustainability throughout the building lifecycle. This study aims to identify the core influencing factors of 6D BIM in BIPV systems and analyze their impact on sustainability performance indicators. To this end, this study used a systematic literature review method to retrieve research literature related to 6D BIM and BIPV from the past decade (2014-2024) from Scopus and Web of Science databases and applied the PRISMA method to screen and analyze 111 highly relevant literature. The results show that the keywords of BIPV-related research have been continuously updated in the past decade, showing the rapid development of 6D BIM in the field of sustainable buildings and its potential in building energy management. Based on the sustainability of BIPV, the study identified five key factors that affect the application of BIPV in sustainable systems: economic and cost factors, policy and social factors, geographical and meteorological factors, design and performance factors, and operation and maintenance factors. In addition, according to the sustainable performance of BIPV, 6D BIM is used as an enhancement factor to improve the sustainability of BIPV. The results of this study provide new insights into the application of 6D BIM in the field of BIPV and provide a scientific basis for stakeholders to develop more efficient implementation strategies.

With the rapid development of China's property industry, residential construction projects face significant schedule delays due to inadequate progress management. It makes it difficult for enterprises to realize the return of funds on time, causing economic losses and reputational damage which brings serious economic losses and loss of corporate reputation. This study aims to identify and prioritize delay factors and propose effective control measures. Qualitative research methods, including semi-structured interviews, are used to collect data from the Yun Yue Dong Fang residential project in China, a case study, and analyze the influencing factors of project schedule management. Like construction, design, materials, finance, communication, non-human elements, planning, and supervision. Results show construction factors (particularly execution efficiency, funding timeliness, and resource allocation) dominate, as evidenced by their highest word frequency in interview coding. Followed by client factors, while the factors related to the design teams, as well as the factors related to the raw material and equipment suppliers have less impact. The study proposes strengthening control through integrated management systems and real-time monitoring, validated by the case study, to mitigate delays. The results of the study provide actionable insights for optimizing schedule management in residential projects as well as the project schedule management of residential development enterprises and enhancing enterprise performance.

The study examines how design quality, indoor air quality, and energy efficiency affect customer satisfaction in eco-friendly houses in Shanghai, China. Further, it examines how environmental awareness mediates community participation and resident satisfaction. A stratified sampling technique is used to collect the data from 742 eligible respondents in public and private eco-residential complexes. The results show that design, air quality, and energy efficiency improve customer satisfaction. At the same time, community engagement partially mediates these correlations, stressing the importance of social cohesion in enhancing residential area quality. Environmental awareness moderated the effects and boosted the happiness benefits of energy efficiency and indoor air quality. This research uses a comprehensive framework that includes psychological, environmental, and social components to make it stand out. Instead of studying green housing benefits in general, it accomplishes this inside China’s urban sustainability program. The results help policymakers, urban planners, and housing authorities make megacity green housing more desirable and livable.

The construction industry is undergoing a significant technological transformation, with Artificial Intelligence (AI) playing a pivotal role in improving efficiency, sustainability, and decision-making. As complex projects evolve, traditional manual methods often lead to inefficiencies, delays, and increased safety risks. AI has emerged as a vital tool for optimizing construction processes, though there remains an incomplete understanding of its applications across various project stages. This study systematically identifies key AI applications that enhance smart construction throughout planning, design, execution, operation, and demolition phases. A Systematic Literature Review (SLR) was employed following the PRISMA framework, leading to the collection of 23 relevant articles from the Web of Science database. Descriptive and bibliometric analyses were conducted using VOSviewer to assess publication trends, research methodologies, geographical distribution, and the co-occurrence of AI-related keywords. Findings indicate that AI significantly aids in predictive analytics for risk assessment, generative design, construction monitoring, safety management, supply chain optimization, energy efficiency, and smart maintenance through digital twins. Furthermore, AI advances real-time site monitoring, waste sorting, and sustainability-driven deconstruction planning, which supports the implementation of the circular economy in construction. This study provides a structured overview of AI’s contribution to smart construction, offering insights to academics, policymakers, and industry professionals on driving the integration of AI in the built environment. The goal is to foster safer, more efficient, and sustainable construction practices in the future.

Due to the boosting demand for sustainable development, the construction industry has shift towards the incorporation of green building practices which are aimed at reducing the effects of climate change. Yet, there is a research gap in which advanced application of digital technologies like Digital Twin (DT) technology has not been fully explored in the integration with green building. This study fills this gap by providing a systematic literature review and identifying the characteristics of DT technology within the green building industry with regards to the construction industry in Malaysia. The research, which adopts an inductive research methodology, gathers data from semi-structured interviews with the industry experts in Malaysia. The study shows that DT technology improves green building performance due to the integration of advanced tools, real time monitoring and management, bi-directional interaction and feedback and creation of digital twin. High fidelity modeling technologies enhance collaboration and communication, performance simulation and optimization, and predictive analysis and error minimization. Simulation base models include comprehensive lifecycle management and sustainable outcomes and user satisfactions. The findings suggest that DT technology plays a crucial role in the effective and efficient management of resources, energy conservation, and the overall efficiency of buildings. Such insights will prove useful to the construction industry stakeholders who will be able to learn about the possibilities that can be unlocked by the DT technology to foster the green building practices as well as support the sustainable development goals.

This research explores the current green site management practices (GSMPs) commonly practiced by contractors. While the construction industry has contributed significantly to Malaysia’s development, it has also raised various environmental concerns. GSMPs are gaining attention as a solution to these issues. Nevertheless, their implementation faces various challenges, such as financial concerns and a lack of knowledge. A qualitative approach was adopted to focus on contractors’ experiences with green practices. Five contractors were randomly selected from the construction sites in Malaysia using the convenience sampling method. These respondents, all at the management level, were well-positioned to provide insights. Data collection continued until no new issues emerged from the interviews. The findings revealed that GSMPs are becoming a current trend within the industry. Contractors are beginning to integrate green practices in their construction activities, focusing on construction site waste management, workforce management, best regulatory practices, site establishment and administration, and site facilities. However, there is a notable lack of awareness and knowledge about these green practices among contractors. The study offers practical implications for the future of GSMPs, highlighting the need for increased understanding and adoption. By elaborating on available practices and their implications, this study aims to encourage broader implementation of GSMPs in construction sites.

Climate warming will have a profound impact on ecosystems, human society and economic development, so mitigating climate warming has become an important challenge facing the world. However, the construction industry is one of the important sources of global carbon emissions, and the use of digital technology had a revolutionary impact on the development of the construction industry. Artificial Intelligence plays an important role in digital technology, and its status and influence are further increasing. However, there is currently no comprehensive explanation of how Artificial Intelligence affects the construction industry’s carbon emissions. This study aims to explore the role of artificial intelligence on carbon emissions in the construction industry from multiple perspectives in the four life cycles of planning, design, operation & maintenance, and tear down. This study also to provide certain guidance for AI's participation in carbon reduction in the construction industry. The search of this study covers the three analysis dimensions of "efficiency improvement", "energy optimization" and "cost control", and analyses the contribution of Artificial Intelligence on carbon emissions in the construction industry by adopting mixed research methods. First, this study systematically analysed 85 publications collected in Scopus and Web of Science databases using PRISMA guidelines. Next, qualitative research method was used where semi-structured interview was use for data collection. Then Analytic Hierarchy Process was used to obtain the research results. Through research, the role of artificial intelligence in carbon reduction in the construction industry is further clarified, and the most obvious role of Artificial Intelligence in each life cycle is efficiency improvement. This research can provide reference and guidance for the use of AI in different life cycles.