The use of new, digitally enabled innovations, such as building information modeling (BIM), raises issues such as the delineation of a competent leader. Even though BIM-based competency assessment models have become essential tools for maximizing the potential values of BIM implementation, the current competency models provide limited focus on leadership aspects that facilitate and enhance the BIM implementation efforts. This paper seeks to identify the specific competencies required for BIM implementation and examines the relationships between these competencies. Thirty-two experts from around the globe investigated a total of 15 leadership competencies under three categories pertaining to intellectual, managerial, and emotional leadership. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was implemented to examine the cause-and-effect relationships among the BIM leadership competencies and fuzzy analytic network process (ANP) was performed to weigh those competencies. Findings show that the intellectual competencies act as the cause group, while managerial and emotional competencies are the effect groups. Moreover, the involving leadership is found to be the more suitable leadership style for BIM professionals, given the current capability and maturity levels of BIM implementation, in order to deal with the required changes throughout the BIM implementation process. This study contributes to the existing body of knowledge in the BIM domain to examine the associated leadership competencies by using the multi-criteria decision-making (MCDM) technique. The results of this research show the relative importance of criteria and sub-criteria, which contributes to further improvement of BIM leadership.

Building Information Modelling (BIM) implementation is a dynamic process and there are a number of influential variables that may change throughout. There is little research on the dynamics of the change environment and the AEC organizations’ approaches to BIM adoption and implementation. A considerable number of BIM enablers have been identified and/or developed in the extant literature. However, stipulating BIM implementation enablers per se provides only a static view that is not adequate for describing effective management of BIM implementation in Architectural, Engineering, and Construction (AEC) organizations. This study is the second part of an ongoing research about BIM implementation enablers. In the first paper “Building Information Modelling (BIM) adoption and implementation enablers in AEC firms: a systematic literature review” (Abbasnejad et al., 2020) the organizational BIM enablers have been identified. The aim of this second paper is to (1) further review and validate the key BIM implementation enablers using both the existing literature and expert interviews, and (2) develop a structural model of the key enablers using the ISM technique to understand the mutual interaction of these enablers and identify the driving enablers and the dependent enablers. Twenty-eight enablers for BIM implementation were initially identified from the literature and subsequent discussion with experts from academia and industry has been conducted to select most key BIM implementation enablers. Eleven enablers were finally chosen based on the literature review and expert interviews and the Interpretive Structural Modeling (ISM) technique has been adopted to evaluate the contextual interrelationships among them. MICMAC (Matrix Impacts Cross-reference Multiplication Applied to a Classification) analysis was employed to classify the eleven enablers based on their dependence and driving power. The results indicate that there is no enabler in the autonomous cluster and this therefore signifies that all enablers are required for the implementation of BIM. BIM leadership and top management support have been identified as the enablers with the highest driving power in the initial stages of the BIM adoption and implementation process and for that reason, these enablers demand a greater priority given that there are other dependent enablers that will be impacted.

Analysis of the properties of diverse pipe materials is a must for delivering the requirements of hydrocarbon pipeline projects. Three of the most commonly used hydrocarbon pipe materials are carbon steel, glass fiber-reinforced plastic (GRP), and glass fiber-reinforced epoxy (GRE). An integrated multicriteria decision-making (MCDM) and lifecycle assessment (LCA) method is utilized to select the most suitable pipe material for hydrocarbon pipeline projects. In this research, the decision-making trial and evaluation laboratory (DEMATEL) analysis is integrated with the analytic network process (ANP) and with LCA. The proposed hybrid decision-making model defines complex causal relationships among different criteria. It can also combine technical, economic, and environmental criteria for pipe material selection in hydrocarbon pipeline projects. Results show that the environmental impacts of carbon steel pipes are higher than other materials in almost every impact category, whereas GRE pipe is the most environmentally friendly of the three alternatives. The subcriterion of global warming potential is significant in the life cycles of all three pipe materials. This innovative model considers a range of relevant technical, economic, and environmental criteria and selects carbon steel pipe as having the highest value among the three alternatives.

Purpose: Exposure to extreme heat poses significant health risks for construction workers due to multiple reasons. However, research on heat-related illnesses (HRIs) in this sector has often relied on statistical methods, limiting insights into causal mechanisms and feature interrelations. This study addresses this gap by employing machine learning (ML) techniques to predict the severity of HRIs (fatal vs non-fatal) and identify critical contributing factors. Design/methodology/approach: A total of 370 Occupational Safety and Health (OSHA) HRI reports are analyzed using natural language processing (NLP) to extract key features. These features are combined with climatic and non-climatic variables. Four ML algorithms – random forest, extreme gradient boosting, k-nearest neighbors and logistic regression – are applied to classify HRIs as fatal and non-fatal. Feature importance and dependencies are explored using SHAP (SHapley Additive exPlanations). Findings: The random forest model achieves the highest accuracy at 70%. Employers' negligence and workers aged over 35 years are key human-centric factors in fatal HRIs. Humid subtropical and hot-summer Mediterranean climate zones are the most hazardous environmental factors. The results suggest that small-scale projects with limited budgets, renovation projects and building projects (residential/commercial) are the key contributing project-related features in the severity of HRIs. Originality/value: This study is among the first attempts to investigate HRI reports specifically in the construction industry by using artificial intelligence techniques. The findings emphasize the need for tailored heat stress prevention programs in construction projects based on their characteristics.