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In the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident.

The developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS).

Through the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved.

The developed 5D-SRAM provides the concerned safety decision-makers with not only all the crucial dimensions that play roles toward the magnitude of safety risks posing threats to the workers involved in construction activities, but also they are given hindsight regarding the importance weights of these dimensions. Additionally, the concerned parties are embellished with the final ranking of safety risks in a more comprehensive way than those of existing assessment methods, leading to sagacious adoption of future prudent strategies for dealing with such risks occurring on construction sites.

Numerous studies have documented the safety risks faced by construction workers including proposals for risk assessment models. However, the dimensions considered by such models are limited, generally constrained to risk event probability combined with risk impact severity. Overlooking other dimensions that are essential towards the calculation of safety risks' magnitude culminates in overshadowing the further adoption of fruitful mitigative actions. To overcome this shortcoming, this study proposes a novel 5D-SRAM.

To come up with a prudent decision on the installation of an appropriate green wall (GW) on buildings, this study presents a novel decision-making algorithm. The proposed algorithm considers the importance of barriers hampering GW adoption, as well as their relationships with regard to different types of GWs existing in a contextual setting.

The proposed methodological approach is based on the integration of qualitative and quantitative techniques by employing focus group discussion, fuzzy-based best-worst method and fuzzy TOPSIS.

Based on the experiences of qualified experts involved in related projects in Hong Kong, the following conclusions are drawn: (1) cost, installation and maintenance-related barriers are perceived to have the highest importance, (2) modular living wall system is the most suitable GW system for the context of Hong Kong and (3) existing barriers are found to have a pivotal role in the ranking of the most suitable GW systems.

The findings provide valuable insight not only for policymakers and stakeholders, but also for establishing a methodological approach that can assist decision-makers in identifying the most beneficial GW system rather than the most applicable one. This could have significant implications and introduce potential changes to the common way of practice within the industry and lay the foundation for wider adoption of GW.

While previous studies have investigated the sustainability-related issues of GW façade applications, the current body of knowledge is deprived of a comprehensive methodological approach for the selection of the most suitable GW systems.

Green walls (GWs), comprising living walls and green facades, have been touted as environmentally friendly products in architectural design. GWs can be viable in every aspect of sustainability; they provide residents of buildings with a wide range of economic, social and environmental benefits. Despite this, the adoption rate of GW is still in its infancy stage, and the existing literature concerning the hindrances inhibiting GW adoption is very limited. To address these gaps, the aim of this paper is to identify and prioritize the hindrances to GW adoption in Hong Kong.

After identifying 17 hindrances through an in-depth review of literature, the fuzzy Delphi method (FDM) is employed to refine the hindrances based on the local context with the help of 21 qualified experts in the field. Subsequently, Fuzzy Parsimonious Analytic Hierarchy Process (FPAHP) is exploited as a recently developed technique to prioritize the identified hindrances.

Results reveal that the most significant hindrances to the adoption of GW are maintenance cost, high installation cost, difficulties in maintenance, sophisticated implementation and inducement to fire. Findings call for scholars to address ways to improve GW installation practices and methods in order to eradicate the hindrances and provide lessons for policymakers, assisting them in facilitating the larger-scale adoption of GW.

Considering the dearth of studies on hindrances to the adoption of GWs, this paper provides a comprehensive outlook of the issue, providing knowledge that can be used as a building block for future scholars within the field. It also provides valuable insights for stakeholders within the construction industry about the hindrances to the adoption of GWs which could direct their efforts toward better implementation of it.

Previous research has demonstrated that Digital Twins (DT) are extensively employed to improve sustainable construction methods. Nonetheless, their uptake in numerous nations is still constrained. This study seeks to identify and examine the digital twin’s implementation barriers in construction building projects to augment operational performance and sustainability.

An iterative two-stage approach was adopted to explore the phenomena under investigation. General DT Implementation Barriers were first identified from extant literature and subsequently explored using primary questionnaire survey data from Hong Kong building industry professionals.

Survey results illustrated that Lack of methodologies and tools, Difficulty in ensuring a high level of performance in real-time communication, Impossibility of directly measuring all data relevant to the DT, need to share the DT among multiple application systems involving multiple stakeholders and Uncertainties in the quality and reliability of data are the main barriers for adopting digital twins' technology. Moreover, Ginni’s mean difference measure of dispersion showed that the stationary digital twin’s barriers adoption is needed to share the DT among multiple application systems involving multiple stakeholders.

The study’s findings offer valuable guidance to the construction industry. They help stakeholders adopt digital twins' technology, which, in turn, improves cost efficiency and sustainability. This adoption reduces project expenses and enhances environmental responsibility, providing companies a competitive edge in the industry.

This research rigorously explores barriers to Digital Twin (DT) implementation in the Hong Kong construction industry, employing a systematic approach that includes a comprehensive literature review, Ranking Analysis (RII) and Ginni’s coefficient of mean difference (GM). With a tailored focus on Hong Kong, the study aims to identify, analyze and provide novel insights into DT implementation challenges. Emphasizing practical relevance, the research bridges the gap between academic understanding and real-world application, offering actionable solutions for industry professionals, policymakers and researchers. This multifaceted contribution enhances the feasibility and success of DT implementation in construction projects within the Architecture, Engineering and Construction (AEC) sector.

The purpose of this study is to identify and analyse the key measurable returning factors, value drivers and strategic benefits associated with building information modelling (BIM) return on investment (ROI). The findings of this study provide researchers and practitioners with up-to-date information in formulating appropriate strategies to quantify the monetary value of BIM. The suggested research agenda provided would also advance what is presently a limited body of knowledge relating to the evaluation of BIM ROI.

To fill the identified gap, this study develops a comprehensive systematic review of mainstream studies on factors affecting BIM ROI published from 2000 to 2020. A total of 23 academic records from different sources such as journals, conference proceedings, dissertation and PhD theses were identified and thoroughly reviewed.

The reported BIM ROI ranged greatly from −83.3% to 39,900%. A total of 5 returning factors, namely, schedule reduction and compliance, productivity improvement, request for information reduction, rework reduction and change orders reduction were identified as the most commonly reported factors that influence BIM ROI. Four quantification techniques including general assumptions-based theoretical model, perceived BIM ROI based on survey, factors affecting BIM ROI with no reported ROI and quantified BIM ROI based on a case study were observed and pointed out in the review, together with their limitations. Finally, three major gaps were raised as the lack of consideration on the likelihood of BIM assisting in a construction project, intangible returning factors influencing BIM-based projects and industry standards in benchmarking BIM ROI.

The outcomes of this study would assist practitioners by providing the current evaluation techniques that address the limitations with BIM investment and present issues relating to the economic evaluation of BIM in the construction industry. It is also expected that presenting a deeper and wider perspective of the research work performed until now will direct a more focussed approach on productivity improvement efforts in the construction industry.

This study identifies and analyses the key measurable returning factors, value drivers and strategic benefits associated with BIM ROI on an industry scale rather than a particular organisation or a project scale.

This research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification technique.

A 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and integrated.

The OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically estimated.

Unlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.

Homebuyers' motivation to purchase the green building (GB) is vital for the widespread adoption of greener practices and for tackling the adverse impact of the built environment. In this regard, through the lenses of qualified and competent GB developers (GBD) and real estate agents (REAs) using judgment sampling technique, this paper aims to evaluate the factors stimulating the purchase intention of Malaysian homebuyers.

The study uses the interval-valued fuzzy Delphi method (IVFDM) to identify influential factors, while the cybernetic fuzzy analytic hierarchy process (CFAHP) for prioritizing the identified factors according to their significance in nationwide promotion of GBs is utilized.

The findings, which are consistent with those reported in the literature and validated by the focus group, uncover the most prominent factors that drive the purchase intention of GB homebuyers. The results indicate that the homebuyers are familiar with the benefits of GB; however, the benefits are not sufficient to overcome the perceived “high cost” of green practices. Hence, homebuyers still seek financial incentives, which is a clear indication that financial issues are the most motivating factor.

Increase in informative promotion of GB is necessary, particularly among prospective homebuyers in developing countries where GB adoption is in its infancy. Frontline players (those who are responsible for wider promotion) can utilize the factors that this study found to be important to identify appropriate steps to increase prospective homebuyers' motivation for purchasing greener dwellings.

The contribution of this paper is threefold. First, review and identification of the most cited determinants that have been reported in the context of the subject. Second, this paper presents the most significant determinants in a developing country setting through the lenses of Malaysian experts in the area. Third, improvement measures could be implemented in Malaysia and other developing countries to promote the GB paradigm with an aim to make the building environment more sustainable.

This study aims to present a comprehensive review, critical analysis and implications of the augmented reality (AR) application and implementation in the construction industry arena and demonstrate the gaps along with the future research agenda.

The construction industry has been under pressure to improve its productivity, quality and sustainability. However, the conventional methods and technologies cannot respond to this industry's ever-growing demands while emerging and innovative technologies such as building information modelling, artificial intelligence (AI), virtual reality (VR) and AR have emerged and can be used to address this gap. AR application has been acknowledged as one of the most impactful technologies in the construction digitalization process. However, a comprehensive understanding of the AR application, its areas of effectiveness and overarching implications in a construction project life cycle remain vague. Therefore, this study uses an integration of systematic literature review and thematic analysis techniques to identify the phases of a construction project life cycle in which AR is the most effective, the current issues and problems of the conventional methods, the augmented parameters, the immediate effects of using AR on each phase and, eventually, the overall influence of AR on the entire project. Nvivo qualitative data analysis software was used to code, categorize and create themes from the collected data. The result of data analysis was used to develop four principal frameworks of the AR applications – design and constructability review session; construction operation; construction assembly; and maintenance and defect inspection and management – and the gap analysis along with the future research agenda.

The findings of this study indicated that the application of AR can be most effective in the following four stages of a project life cycle: design and constructability review session; construction operation; construction assembly; and site management and maintenance, including site management and defect inspection. The results also showed that the application of AR technology in the construction industry can align and address building industry objectives by various elements such as: reducing project costs through the application of digital technologies, saving time, meeting deadlines and reduction in project delays through integrated, live scheduling and increased safety and quality of the construction work and workers.

One of the main limitations of this study was the lack of materials and resources on the downfalls and shortcomings of using immersive technologies, AR, in the construction project life cycle. In addition, most of the reviewed papers were focused on the experiments with simulations and in the lab environment, rather than real experiments in real construction sites and projects. This may cause limitations and inaccuracy of the collected and reported data.

The results of this study indicated that the application of AR technology in construction industry can align and address building industry objectives by various elements such as: reducing project costs through the application of digital technologies; saving time; meeting deadlines and reduction in project delays through integrated, live scheduling; and increased safety and quality of the construction work and workers.

Application of AR in the various stages of a project life cycle can increase the safety and quality of the construction work and workers.

The reviewed literature indicated that substantial research and studies are yet to be done, to demonstrate the full capacity and impact of these emerging technologies in the field. The collected data and literature indicate that amongst the digital technologies, AR is one of the least researched topics in the field. Therefore, this study aims to examine the application of AR in construction projects’ life cycle to identify the stages and practices of a project life cycle where AR and its capabilities can be exploited and to identify the respective problems and issues of the conventional methods and the ways in which AR can address those shortcomings. Furthermore, this study focuses on identifying the overall outcome of AR applications in a construction project in terms of cost and time efficiency, process precision and safety.

Proper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.

In this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.

Considering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.

The outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.

This paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.

International construction joint ventures (ICJVs) implementation is plagued with several barriers, full understanding of which is still lacking due to a lack of an in-depth exploration of them, particularly in developing countries. To fill this knowledge gap, this study aims to investigate the critical barriers to the success of ICJVs hosted in developing countries by examining the Ghanaian case.

This study builds on a previous study that identified 37 barriers factors to ICJVs success via a systematic literature review. Through expert interviews, 34 potential barriers were identified, and a two-round survey was conducted with 84 ICJVs practitioners in Ghana. The data collected was analyzed using the combination of a multidimensional fuzzy logic method and confirmatory factor analysis.

Results showed that 22 barriers were critical. The top five most critical barriers were “lack of preparedness to accept company philosophy,” “competing objectives,” “opportunistic behavior of parties,” “conflicts” and “lack of management control.” Furthermore, the results uncovered and confirmed five significant underlying components for the 22 critical barriers, namely, organizational-related, cultural-related, knowledge-related, individual-related and logistics-related barriers.

The findings could be useful to ICJVs practitioners and policymakers in developing suitable strategies for the successful implementation of ICJVs. Further, foreign firms aiming to execute and promote ICJVs in Ghana could have prior knowledge of the critical barriers and prepare for them.

This study empirically analyzed the individual levels of barriers criticalities in ICJVs context and from a specific-country perspective – the developing country of Ghana – rather than in the context of construction joint ventures and from a cross-country perspective in extant studies.