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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).
- Type: Journal Article
- Keywords: accident prevention; construction safety; motion trajectory; risk diagnosis; TOPSIS
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13673
- Abstract:
Workers' motion trajectories or spatial tracks on construction sites contain useful safety-related information. Existing safety management research on worker trajectory typically analyzes the interactions between worker motion trajectory and risk sources. The historical accident-free zone of a group of workers is a reflection of the potential safety zones on a construction site. Few studies have investigated construction workers' trajectory safety risks from the integrated perspectives of group and hazard sources. Therefore this study developed a novel and integrated safety risk diagnosis method combining hazard source and group movement distributions to fully utilize the phone Global Positioning System (GPS) trajectory information of construction workers. The proposed method diagnoses workers' risk exposures by considering workers' trajectories in unsafe and safe areas. In addition, the method uses expert confidence and comprehensive decision indexes to determine the feature weights. Furthermore, the proposed safety risk diagnosis method adopts the grey optimization Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) diagnosis model to diagnose workers' risk management priorities and behavior adjustment direction. An actual project site was used to assess the performance of the proposed diagnostic method. The results show that the developed method provides a quantitative means for project managers to measure workers' spatial-temporal risk exposure, diagnose safety risks, and plan for safety controls. The proposed integrated method provides a new perspective to make full use of workers' trajectory information and helps to provide practical and specific data-driven safety guidance for construction managers and workers.
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).
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).