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Aliasgari, R, Fan, C, Li, X, Golabchi, A and Hamzeh, F (2024) MOCAP and ai-based automated physical demand analysis for workplace safety. Journal of Construction Engineering and Management, 150(07).
Chae, J, Hwang, S and Kang, Y (2024) Measuring habituation to auditory warnings using behavioral and physiological data. Journal of Construction Engineering and Management, 150(07).
Chen, H and Chan, I Y S (2024) Effects of automation and transparency on human psychophysiological states and perceived system performance in construction safety automation: An electroencephalography experiment. Journal of Construction Engineering and Management, 150(07).
Dahalan, N H, Rahman, R A, Hassan, S H and Ahmad, S W (2024) Public assessment for environmental management plan implementation: Comparative study of performance indicators of road and highway construction projects. Journal of Construction Engineering and Management, 150(07).
Deep, S, Sonkar, N, Yadav, P, Vishnoi, S, Bhoola, V and Kumar, A (2024) Factors influencing the mental health of white-collar construction workers in developing economies: Analytical study during the COVID-19 pandemic in India. Journal of Construction Engineering and Management, 150(07).
Deng, S, Ni, P, Zhu, H, Cai, Y and Pan, Y (2024) Artificial cognition to predict and explain the potential unsafe behaviors of construction workers. Journal of Construction Engineering and Management, 150(07).
Eliwa, H K, Jelodar, M B, Poshdar, M and Zavvari, A (2024) Organizational infrastructure and information and communication technology infrastructure alignment in construction organizations. Journal of Construction Engineering and Management, 150(07).
Flores, M and Mourgues, C (2024) Impact of using a formalized methodology for conflict detection based on 4D-BIM. Journal of Construction Engineering and Management, 150(07).
Huang, H, Hu, H, Xu, F and Zhang, Z (2024) Kinesiology-inspired assessment of intrusion risk based on human motion features. Journal of Construction Engineering and Management, 150(07).
King Lewis, A and Shan, Y (2024) Persistence of women in the construction industry. Journal of Construction Engineering and Management, 150(07).
Lei, T, Seo, J, Liang, K, Xu, J, Li, H, Zhou, Y, Khan, M and Heung, K H (2024) Lightweight active soft back exosuit for construction workers in lifting tasks. Journal of Construction Engineering and Management, 150(07).
Li, C Z, Wen, S, Yi, W, Wu, H and Tam, V W Y (2024) Offsite construction supply chain challenges: An integrated overview. Journal of Construction Engineering and Management, 150(07).
Liu, C, González, V A, Lee, G, Cabrera-Guerrero, G, Zou, Y and Davies, R (2024) Integrating the last planner system and immersive virtual reality: Exploring the social mechanisms produced by using LPS in projects. Journal of Construction Engineering and Management, 150(07).
Ma, Z, Liu, W, Li, C, Sang, Y, Zhang, Y, Li, G and Xu, Y (2024) Research on energy-saving control strategy of loader based on intelligent identification of working stages. Journal of Construction Engineering and Management, 150(07).
Naji, K K, Gunduz, M and Mansour, M M (2024) Development of an integrated hybrid risk assessment system for construction disputes during the preconstruction phase using the Delphi method. Journal of Construction Engineering and Management, 150(07).
Qin, L, He, Q, Fu, X, Wang, Y and Wang, G (2024) Peer effects on corporate social responsibility engagement of Chinese construction firms through board interlocking ties. Journal of Construction Engineering and Management, 150(07).
Shrestha, S, Shan, Y and Goodrum, P M (2024) Identification of best practices in project bundling for state dots using semistructured interviews. Journal of Construction Engineering and Management, 150(07).
Song, S H, Choi, J O and Cho, H (2024) Transportation-induced impact on a prefinished volumetric modular house using trailer bogie: Case study. Journal of Construction Engineering and Management, 150(07).
Tan, Y, Deng, T, Zhou, J and Zhou, Z (2024) Lidar-based automatic pavement distress detection and management using deep learning and BIM. Journal of Construction Engineering and Management, 150(07).
Tran, H, Robert, D, Gunarathna, P and Setunge, S (2024) Estimating cost of bridge closure for bridge network rehabilitation priorities. Journal of Construction Engineering and Management, 150(07).
Wei, F, Hwang, B G, Zainal, N S B and Zhu, H (2024) Trust, team effectiveness, and strategies: A comparative study between virtual and face-to-face teams. Journal of Construction Engineering and Management, 150(07).
Wu, J, Ye, Y and Du, J (2024) Autonomous drones in urban navigation: Autoencoder learning fusion for aerodynamics. Journal of Construction Engineering and Management, 150(07).
Xia, X, Xiang, P, Khanmohammadi, S, Gao, T and Arashpour, M (2024) Predicting safety accident costs in construction projects using ensemble data-driven models. Journal of Construction Engineering and Management, 150(07).
- Type: Journal Article
- Keywords: construction safety management; data-driven; ensemble data-driven models; machine learning; safety accident costs
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-14397
- Abstract:
The construction industry suffers from frequent and expensive safety accidents, significantly affecting construction project performance. Numerous data-driven classification models have been developed to categorize construction accident outcomes. While critical influencing factors provide insights for safety prevention, existing models have given less attention to the cost of accidents-an important indicator influencing management decisions. This study aims to develop accident cost prediction models that examine crucial precursors of safety accidents, offering guidance for construction safety prevention from a financial perspective. This study collected 1,606 accident reports from the Chinese construction industry between 2005 and 2022 to address this gap. Three ensemble data-driven methods, namely random forest, extreme gradient boosting regressor (XGBoost), and natural gradient boosting regressor (NGBoost) were employed to develop accident cost prediction models. Based on the performance comparison, the random forest regression model for accident cost was determined to be the best prediction model. To extract the critical attributes affecting safety accident costs, this study utilized shapely additive explanations (SHAP) value to analyze the sensitivity and influence of input variables of data-driven models. The findings showed that collapse has the greatest impact on accident costs, as indicated by the highest mean SHAP value, followed by falling from height. Furthermore, factors such as year, safety supervision, drawing, and construction plan are noteworthy in affecting accident cost prediction. Safety department, protection, and work conditions hold a slightly higher degree of influence compared to contracting arrangement, safety culture, safety supervision, training and examination, and mechanical equipment on the model output. This study provides a dimension that might be overlooked in the investigation of safety accidents in the construction industry and the insights provided by findings will contribute to the development of targeted safety accident prevention strategies.
Xiang, Q, Liu, Y, Goh, Y M, Ye, G and Safiena, S (2024) Investigating the impact of hazard perception failure on construction workers' unsafe behavior: An eye-tracking and thinking-aloud approach. Journal of Construction Engineering and Management, 150(07).
Yan, D, Wang, C C and Sunindijo, R Y (2024) Framework for promoting women's career development across career stages in the construction industry. Journal of Construction Engineering and Management, 150(07).