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Al-Ghzawi, M and El-Rayes, K (2023) Optimizing the planning of airport airside expansion projects to minimize air traffic disruptions and construction cost. Journal of Construction Engineering and Management, 149(04).

Barros, B A F S and Sotelino, E D (2023) Constructability and sustainability studies in conceptual projects: A BIM-based approach. Journal of Construction Engineering and Management, 149(04).

Guo, H, Zhang, Z, Yu, R, Sun, Y and Li, H (2023) Action recognition based on 3D skeleton and LSTM for the monitoring of construction workers' safety harness usage. Journal of Construction Engineering and Management, 149(04).

Hassan, F U, Le, T and Le, C (2023) Automated approach for digitalizing scope of work requirements to support contract management. Journal of Construction Engineering and Management, 149(04).

Jeon, J, Zhang, Y, Yang, L, Xu, X, Cai, H and Tran, D (2023) Risk breakdown matrix for risk-based inspection of transportation infrastructure projects. Journal of Construction Engineering and Management, 149(04).

Koc, K, Ekmekcioǧlu, Ö and Gurgun, A P (2023) Developing a national data-driven construction safety management framework with interpretable fatal accident prediction. Journal of Construction Engineering and Management, 149(04).

  • Type: Journal Article
  • Keywords: construction safety management; interpretable artificial intelligence; machine learning; occupational accidents; occupational health and safety
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-12848
  • Abstract:
    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.

Li, Y, Ning, Y and Rowlinson, S (2023) Social control in outsourced architectural and engineering design consulting projects: Behavioral consequences and motivational mechanism. Journal of Construction Engineering and Management, 149(04).

Nigra, M and Bossink, B (2023) Cooperative learning in green building demonstration projects: Insights from 30 innovative and environmentally sustainable demonstrations around the world. Journal of Construction Engineering and Management, 149(04).

Pushpakumara, B H J, Gunasekara, M T and Gannile, Y M T D (2023) Variation of mechanical and chemical properties of old and new clay bricks. Journal of Construction Engineering and Management, 149(04).

Shiha, A and Dorra, E M (2023) Resilience index framework for the construction industry in developing countries. Journal of Construction Engineering and Management, 149(04).

Shirazi, D H and Toosi, H (2023) Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(04).

Xia, N, Griffin, M A, Xie, Q and Hu, X (2023) Antecedents of workplace safety behavior: Meta-analysis in the construction industry. Journal of Construction Engineering and Management, 149(04).

Xu, W and Wang, T K (2023) Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method. Journal of Construction Engineering and Management, 149(04).