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Construction 4.0 is witnessing exponential growth in digital twin (DT) technology developments and applications, revolutionizing the adoption of building information modelling (BIM) and other emerging technologies used throughout the built environment lifecycle. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, the DT enhances this with real-time data for integrating cyber-physical systems, enabling live asset monitoring and better decision making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study provides a holistic review of the existing research focusing on the critical components responsible for developing the applications of DT technology in the construction industry. It highlights five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, non-technical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices. It also pinpoints the latest developments in AI, namely the large language model (LLM) and retrieval-augmented generation (RAG)’s implications for DT education, policies, and the construction industry’s practices.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

The discussion on employee burnout (BOT) has existed in academic literature for a long time. While BOT was identified as a personal issue, there is a lack of a system approach solution. In this regard, a milestone was achieved in 2019 when the World Health Organization (WHO) introduced a new definition of BOT, recognising it as an occupational phenomenon, thereby placing a greater responsibility on organisational leadership to manage it. Since then, different leadership models have been proposed by scholars to reduce the effect of BOT in an organisation. Humble leadership (HL), as a people-first approach, has recently gained importance in organisational management literature. Studies have indicated that HL significantly influences different employee outcomes. However, research on the significance of HL to mitigate BOT of employees was scarce—similarly, on the underlying mechanisms of how and why. While HL has the potential to reduce BOT in an organisation, little or no research has studied it. Acknowledging these knowledge gaps, the basic aim of this study is to enrich the existing body of knowledge by proposing HL as an effective organisational management strategy to reduce the effect of BOT in the healthcare sector. The study introduces two mediators, work engagement (WREN) and subjective wellbeing (SUBW), to explain the underlying mechanism between HL and BOT. The conditional indirect role of altruism (ALM) was also tested. The data were obtained from hospital employees by employing a survey method (questionnaire, n = 303). Structural equation model (SEM) was considered for testing the hypothesised model to study the interrelationships between variables. The results confirmed that the manifestation of HL in an organisation reduces BOT significantly, and WREN and SUBW mediate this relationship. The study also demonstrates the buffering effect of ALM in the above-proposed relationships. The empirical findings offer multiple contributions in theory and practice, among which the most important one was to realise the profound importance of HL in reducing the effect of BOT in healthcare management.

Various researchers have explored the adverse effects of walled buildings on human health. However, few of them have examined the relationship between walled buildings and private housing estates in Hong Kong. This study endeavors to fill the research gap by exploring the connections among walled-building effects, housing features, macroeconomic factors, and housing prices in private housing estates. Specifically, it reveals the relationship between walled buildings and housing prices. Eight privately owned housing estates are selected with a total of 11,365 observations. Results are analyzed to study the factors that affect the housing price. Firstly, unit root tests are carried out to evaluate if the time series variables follow the unit root process. Secondly, the relationship between walled buildings and housing price is examined by conducting an artificial neural network. We assumed that the housing price reduces due to walled-building effects, given that previous literature showed that heat island effect, and blockage of natural light and views, are common in walled-building districts. Moreover, we assume that housing price can also be affected by macroeconomic factors and housing features, and these effects vary among private housing estates. We also study these impacts by using the two models. Recommendations and possible solutions are suggested at the end of the research paper.