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Circular economy (CE) is increasingly used as a system solution framework for tackling climate change. Existing CE solutions have been found effective in other sectors but ineffective in the construction sector (CS) due to the inability to account for unique CS dynamics that are essential for its success. With CS being one of the worst polluters, this research aimed to investigate critical success factors (CSFs) and a practical CE implementation framework for the sector.

Mixed methods research involving descriptive surveys of construction professionals in Kazakhstan, Malaysia and Turkey was used to explore priority dimensions of McKinsey’s ReSOLVE (“regenerate, share, optimize, loop, virtualize and exchange”) circulatory framework, and the associated CSFs that underpin successful implementation of CE in the sector. Empirical data were analyzed using the fuzzy analytical hierarchy process (F-AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm.

Results showed that “Optimize” and “Loop” were the most critical of the six dimensions of CE implementation framework for successful circularity transition of the CS in each case study country. Twenty-seven CSFs identified in the study were clustered into seven key action areas for successful rollout of the ReSOLVE framework, namely, legislative and documentation, financial, design, external/stakeholder, technological, internal and construction/production/waste management.

The study makes a unique contribution to existing knowledge by exploring ReSOLVE-based CSFs for successful CE implementation in the CS through the lenses of experienced construction practitioners and experts in developing countries. The findings are expected to provide a deeper insight into the most significant CSFs to be focused on with the limited resources available to decision-makers in the CS. The findings would also inform regulatory policy reformations aimed at facilitating greater rate of implementation of CE in the construction value chain.

The study – the first of its kind – established and validated a wholistic and construction-specific CE implementation framework to guide built environment professionals and policymakers in formulating a roadmap for successful CS’s transition to circularity and hopefully paving the way for improved economic, social and environmental performance of the sector.

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.

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.

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.

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.

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.

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.

Building information modeling (BIM) is a prominent concept to digitalize data collection and analysis processes. Small and medium-sized enterprises (SMEs) account for a considerable percentage of the works performed in the construction industry. The adoption rate of BIM by SMEs is still, however, not at the desired level in the New Zealand construction industry. This study aims to evaluate barriers to BIM implementation for SMEs in the New Zealand construction industry.

This study adopted four-step methodology to evaluate barriers to BIM adoption for SMEs. First, a comprehensive literature review, followed by a focus group discussion was performed to identify barriers to BIM adoption. Then, analytical hierarchy process (AHP) was used to assess identified barriers. Finally, experts’ agreements (both internal and external) were ensured by consistency analysis and Kendall’s coefficient of concordance (Kendall’s W) tests.

The findings indicate that (1) interoperability between software platforms, (2) lack of government mandate on BIM usage at project level, (3) high cost of acquiring the software and licensing required to use BIM and (4) lack of client demand for adopting BIM were the most significant barriers in terms of technological, governmental, resource and cultural categories, respectively. Further investigation of the expert evaluation showed strong consistencies (each expert separately) and agreements (among experts) in each AHP matrix.

Primary focus should be training of local market (particularly SMEs) professionals as the shortage in qualified professionals makes the country-wide adoption challenging. The publicity in the local market can help SMEs understand how BIM is leveraged for further improvements in project performance.

Overall, this research not only provides a roadmap for the widespread adoption of BIM within SMEs in New Zealand through analysis of the barriers encountered but also highlights the power that policymakers hold over the mass adoption of BIM within SMEs.

Implementing building information modeling (BIM) in construction projects offers many benefits. However, the use of BIM in project cost management is still limited. This study aims to review the current trends in the application of BIM in project cost management.

This study systematically reviews the literature on the application of BIM in project cost management. A total of 46 related articles were identified and analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

Eighteen approaches to applying BIM in project cost management were identified. The approaches can be grouped into cost control and cost estimation. Also, BIM can be applied independently or integrated with other techniques. The integrated approaches for cost control include integration with genetic algorithms, Monte Carlo simulation, lean construction, integrated project delivery, neural network and value engineering. On the contrary, integrated approaches for cost estimation include integration with cost-plus pricing, discrepancy analysis, construction progress curves, estimation standards, algorithms, declarative mappings, life cycle sustainability assessment, ontology, Web-based frameworks and structured query language.

To the best of the authors’ knowledge, this study is the first to systematically review prior literature on the application of BIM in project cost management. As a result, the study provides a comprehensive understanding of the current state of the art and fills the literature gap. Researchers and industry professionals can use the study findings to increase the benefits of implementing BIM in construction projects.