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Albeaino, G, Brophy, P, Jeelani, I, Gheisari, M and Issa, R R A (2023) Impact of drone presence on construction individuals working at heights. Journal of Construction Engineering and Management, 149(11).
Chadee, A A, Martin, H, Chadee, X T, Bahadoorsingh, S and Olutoge, F (2023) Root cause of cost overrun risks in public sector social housing programs in sids: Fuzzy synthetic evaluation. Journal of Construction Engineering and Management, 149(11).
Duan, P, Zhou, J and Goh, Y M (2023) Safety risk diagnosis based on motion trajectory for construction workers: An integrated approach. Journal of Construction Engineering and Management, 149(11).
Erk, E Y, Budayan, C, Koc, K and Tokdemir, O B (2023) Value creation in PPP projects undertaken in the Turkish healthcare industry. Journal of Construction Engineering and Management, 149(11).
Hsu, C L, Wang, J T and Hou, H Y (2023) A blockchain-based parametric model library for knowledge sharing in building information modeling collaboration. Journal of Construction Engineering and Management, 149(11).
Lim, H W and Francis, V (2023) A conceptual model of cognitive and behavioral processes affecting mental health in the construction industry: A systematic review. Journal of Construction Engineering and Management, 149(11).
Mostofi, F, Toǧan, V, Başaǧa, H B, Çltlpltloǧlu, A and Tokdemir, O B (2023) Multiedge graph convolutional network for house price prediction. Journal of Construction Engineering and Management, 149(11).
Tang, Y and Yao, H (2023) Watch out for the hidden costs of subcontracting in construction projects: The impacts of subcontractor dispersion. Journal of Construction Engineering and Management, 149(11).
Wang, D, Huang, R, Qiao, Y, Sheng, Z, Li, K and Zhao, L (2023) How perceived leader-member exchange differentiation affects construction workers' safety citizenship behavior: Organizational identity and felt safety responsibility as mediators. Journal of Construction Engineering and Management, 149(11).
Wang, S, Kim, M, Hae, H, Cao, M and Kim, J (2023) The development of a rebar-counting model for reinforced concrete columns: Using an unmanned aerial vehicle and deep-learning approach. Journal of Construction Engineering and Management, 149(11).
Wang, Z, He, Q, Locatelli, G, Wang, G and Li, Y (2023) Exploring environmental collaboration and greenwashing in construction projects: Integrative governance framework. Journal of Construction Engineering and Management, 149(11).
Watton, J, Unterhitzenberger, C, Locatelli, G and Invernizzi, D C (2023) The cost drivers of infrastructure projects: Definition, classification, and conceptualization. Journal of Construction Engineering and Management, 149(11).
Wu, H, Han, Y, Zhang, M, Abebe, B D, Legesse, M B and Jin, R (2023) Identifying unsafe behavior of construction workers: A dynamic approach combining skeleton information and spatiotemporal features. Journal of Construction Engineering and Management, 149(11).
Wu, L, Mohamed, E, Jafari, P and Abourizk, S (2023) Machine learning-based Bayesian framework for interval estimate of unsafe-event prediction in construction. Journal of Construction Engineering and Management, 149(11).
- Type: Journal Article
- Keywords: Bayesian inference; construction safety management; credible interval; interval estimate; machine learning
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13549
- Abstract:
Construction safety is a critical concern for industry and academia, and numerous models and algorithms have been developed to predict incidents or accidents to facilitate proactive decision-making. However, previous studies have been limited due to the inability to account for uncertainties because predictions are given as a single value (i.e., Yes or No) and the failure to integrate subjective judgment. To address these limitations, this research proposes a machine learning-based Bayesian framework for predicting construction incidents using interval estimates. This framework combines a state-of-the-art machine-learning algorithm with a binary Bayesian inference model to develop an incident predictor that considers a range of project characteristics and conditions. Notably, this framework also is capable of incorporating historical or subjective judgment through prior selection and outputs the unsafe event prediction as an interval of possibilities, thus accounting for various uncertainties. The efficacy of our framework was demonstrated in a real-life case study, showcasing its practical implications for proactive decision-making and risk management in the construction industry and representing a valuable contribution to the field of construction safety.
Wu, S, Yu, L, Cao, T, Yuan, C and Du, Y (2023) How dependence asymmetry and explicit contract shape contractor-subcontractor collaboration: A psychological perspective of fairness. Journal of Construction Engineering and Management, 149(11).
You, H, Xu, F and Du, J (2023) Improved boundary identification of stacked objects with sparse lidar augmentation scanning. Journal of Construction Engineering and Management, 149(11).
Zheng, X, Chen, J, Xia, B, Skitmore, M and Zeng, S (2023) Understanding the megaproject social responsibility network among stakeholders: A reciprocal-exchange perspective. Journal of Construction Engineering and Management, 149(11).
Zhou, Q, Deng, X, Hwang, B G, Mahmoudi, A and Liu, Y (2023) Integrating the factors affecting knowledge transfer within international construction projects: Individual and team perspectives. Journal of Construction Engineering and Management, 149(11).