Numerous inherent risks impose a systematic risk identification approach to achieve supply chain performance goals in construction projects. This paper investigates supply chain risks in construction projects with respect to life cycle and corresponding stakeholders. The research was organized with two stages. In the first stage, a systematic literature review accompanied by a content analysis was conducted based on risks in construction supply chains (CSCs) to identify research gaps, suggest future research directions, and support subsequent steps. Then, three focus group discussions (FGDs) were performed by industry representatives to (1) specify life cycle risks in CSCs, (2) associate specified risks to related stakeholders, and (3) ensure the role of each stakeholder in CSCs. Results of the content analysis indicate that relationship, communication, and sustainability are the most frequently examined research areas, while innovation and organizational learning, supplier vulnerability, and negotiation are overlooked in the literature. Based on sessions of FGDs, 135 risks were categorized into 10 phases of construction supply chain life cycle (SCLC) as requirement and demand, design, tendering and contracting, manufacturing, logistics, hand over, inventory, installation, winding up, and control and maintenance. Main contractor, subcontractor, supplier, and client were found as the critical decision entities in tendering and contracting, installation, logistics, and requirement and demand phases, respectively. The findings can be useful for researchers by indicating research gaps, and for construction companies seeking to increase their supply chain performance through life cycle and stakeholder approaches.

Purpose: As a remedy to usually voluminous, complicated and not easily readable construction contracts, smart contracts can be considered as an effective and alternative solution. However, the construction industry is merely known as a frontrunner for fast adoption of recent technological advancements. Numerous administrative risks challenge construction companies to implement smart contracts. To highlight this issue, this study aims to assess the administrative risks of smart contract adoption in construction projects.Design/methodology/approach: A literature survey is conducted to specify administrative risks of smart contracts followed by a pilot study to ensure that the framework is suitable to the research question. The criteria weights are calculated through the fuzzy analytical hierarchy process method, followed by a sensitivity analysis based on degree of fuzziness, which supports the robustness of the developed hierarchy and stability of the results. Then, a focus group discussion (FGD) is performed to discuss the mitigation strategies for the top-level risks in each risk category.Findings: The final framework consists of 27 sub-criteria, which are categorized under five main criteria, namely, contractual, cultural, managerial, planning and relational. The findings show that (1) regulation change, (2) lack of a driving force, (3) works not accounted in planning, (4) shortcomings of current legal arrangements and (5) lack of dispute resolution mechanism are the top five risks challenging the adoption of smart contracts in construction projects. Risk mitigation strategies based on FGD show that improvements for the semi-automated smart contract drafting are considered more practicable compared to full automation.Originality/value: The literature is limited in terms of the adoption of smart contracts, while the topic is receiving more attention recently. To support easy prevalence of smart contracts, this study attempts the most challenging aspects of smart contract adoption.

Purpose: Completing construction projects within the planned schedule has widely been considered as one of the major project success factors. This study investigates the use of technologies to address delays in construction projects and aims to address three research questions (1) to identify the adopted technologies and proposed solutions in the literature, (2) to explore the reasons why the delays cannot be prevented despite disruptive technologies and (3) to determine the major strategies to prevent delays in construction projects.Design/methodology/approach: In total, 208 research articles that used innovative technologies, methods, or tools to avoid delays in construction projects were investigated by conducting a comprehensive literature review. An elaborative content analysis was performed to cover the implemented technologies and their transformation, highlighted research fields in relation to selected technologies, focused delay causes and corresponding delay mitigation strategies and emphasized project types with specific delay causes. According to the analysis results, a typological framework with appropriate technological means was proposed.Findings: The findings revealed that several tools such as planning, imaging, geo-spatial data collection, machine learning and optimization have widely been adopted to address specific delay causes. It was also observed that strategies to address various delay causes throughout the life cycle of construction projects have been overlooked in the literature. The findings of the present research underpin the trends and technological advances to address significant delay causes.Originality/value: Despite the technological advancements in the digitalization era of Industry 4.0, many construction projects still suffer from poor schedule performance. However, the reason of this is questionable and has not been investigated thoroughly.

Occupational accidents are frequent in the construction industry, containing significant risks in the working environment. Therefore, early designation, taking preventive actions, and developing a proactive safety risk management plan are of paramount significance in managing safety issues in the construction industry. This study aims to develop a national data-driven safety management framework based on accident outcome prediction, which helps anatomize precursors of fatalities and thereby minimizing fatal accidents on construction sites. A national data set comprising 338,173 occupational accidents recorded in the construction industry across Turkey was used to develop a data-driven model. The random forest algorithm coupled with particle swarm optimization was used for the prediction and the interpretability of the proposed model was augmented through the game theory–based Shapley additive explanations (SHAP) approach. The findings showed that the proposed algorithm achieved satisfactory model performances for detecting construction workers who might face a fatality risk. The SHAP analysis results indicated that both company (such as number of past accidents and workers in the company) and worker-related (such as age, daily wage, experience, shift, and past accident of the workers) attributes were influential in identifying fatalities by detecting which workers might face fatal accidents under which conditions. A construction safety management plan was developed based on the analysis results, which can be used on construction sites to detect workers/conditions that are most susceptible to fatalities. The findings of the present research are expected to contribute to orchestrating effective safety management practices in construction sites by characterizing the root causes of severe accidents.

Purpose: Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.Design/methodology/approach: Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.Findings: The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.Practical implications: The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.Social implications: Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.Originality/value: The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

Purpose: Competent contractors are one of the critical stakeholders to achieve targeted sustainability objectives in green building (GB) projects. Prior to contractor selection, prequalification is an important step, which requires contractors with certain capabilities in addition to traditional features. This study aims to develop a systematic and practical model for prequalification in GB projects using a multi-criteria decision-making (MCDM) approach by adopting the analytical hierarchy process (AHP).Design/methodology/approach: TheAHP model with 8 main criteria groups and 25 sub-criteria is structured based on literature review and professional opinions accompanied by a pilot study. Then, interviews with experts, who are experienced in the development and application phases of GB projects in Turkey, are arranged to collect judgements. The agreement levels between different groups of experts are analysed via Pearson's and Spearman's rank correlation coefficient. Model applicability is tested on six hypothetical contractors for practicality.Findings: The results show that i) financial capabilities, ii) legal status and iii) sustainability groups are the top three main criteria, while i) compliance with schedule requirements of the client, ii) current legal status including suits, iii) negative litigation history records, iv) contractor's compliance capacity to client's sustainability checklist for the proposed project and v) sustainability with lower life cycle cost (durability, maintenance, constructability) are the top five sub-criteria.Originality/value: There is a gap in the literature analysing contractor prequalification phase in GB projects. This study attempts to fill this lack provided with a practical evaluation tool.