Safety Risk Diagnosis Based on Motion Trajectory for Construction Workers: An Integrated Approach
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 11
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is funded by the National Natural Science Foundation of China (No. 72171224), the Humanities and Social Sciences Foundation of China’s Education Ministry (No. 19YJAZH122), Everloyal-Innovation Fund for Ph.D. Candidate in Neuromanagement in Engineering (No. Everloyal-NeuroME-2023006), and the Doctor Joint-Training Program of China Scholarship Council (No. 202106420087).
References
Arslan, M., C. Cruz, and D. Ginhac. 2019. “Semantic trajectory insights for worker safety in dynamic environments.” Autom. Constr. 106 (Oct): 102854. https://doi.org/10.1016/j.autcon.2019.102854.
Birch, C. P. D., S. P. Oom, and J. A. Beecham. 2007. “Rectangular and hexagonal grids used for observation, experiment and simulation in ecology.” Ecol. Modell. 206 (3–4): 347–359. https://doi.org/10.1016/j.ecolmodel.2007.03.041.
Cai, J., Y. Zhang, L. Yang, H. Cai, and S. Li. 2020. “A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites.” Adv. Eng. Inf. 46 (Oct): 101173. https://doi.org/10.1016/j.aei.2020.101173.
Celik, E., and M. Gul. 2021. “Hazard identification, risk assessment and control for dam construction safety using an integrated BWM and MARCOS approach under interval type-2 fuzzy sets environment.” Autom. Constr. 127 (Jul): 103699. https://doi.org/10.1016/j.autcon.2021.103699.
Chen, H., X. Luo, Z. Zheng, and J. Ke. 2019. “A proactive workers’ safety risk evaluation framework based on position and posture data fusion.” Autom. Constr. 98 (Feb): 275–288. https://doi.org/10.1016/j.autcon.2018.11.026.
Dong, Y., and D. Pi. 2018. “Novel Privacy-preserving algorithm based on frequent path for trajectory data publishing.” Knowledge-Based Syst. 148 (May): 55–65. https://doi.org/10.1016/j.knosys.2018.01.007.
Duan, P., and J. Zhou. 2022. “A science mapping approach-based review of near-miss research in construction.” Eng. Constr. Archit. Manage. 30 (6): 2582–2601. https://doi.org/10.1108/ECAM-09-2021-0797.
Duan, P., J. Zhou, and L. Fan. 2022a. “A construction worker movement style portrayal approach considering safety risks.” J. Railw. Sci. Eng. https://doi.org/10.19713/j.cnki.43-1423/u.T20221669.
Duan, P., J. Zhou, and W. Fan. 2022b. “Safety tag generation and training material recommendation for construction workers: A persona-based approach.” Eng. Constr. Archit. Manage. https://doi.org/10.1108/ECAM-12-2021-1143.
Duan, P., J. Zhou, and Y. M. Goh. 2023. “Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory.” Adv. Eng. Inf. 56 (May): 101990. https://doi.org/10.1016/j.aei.2023.101990.
El-Rayes, K., and A. Khalafallah. 2005. “Trade-off between safety and cost in planning construction site layouts.” J. Constr. Eng. Manage. 131 (11): 1186–1195. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:11(1186).
García-Cascales, M. S., and M. T. Lamata. 2012. “On rank reversal and TOPSIS method.” Math. Comput. Modell. 56 (5–6): 123–132. https://doi.org/10.1016/j.mcm.2011.12.022.
Golovina, O., J. Teizer, and N. Pradhananga. 2016. “Heat map generation for predictive safety planning: Preventing struck-by and near miss interactions between workers-on-foot and construction equipment.” Autom. Constr. 71 (Mar): 99–115. https://doi.org/10.1016/j.autcon.2016.03.008.
He, C., G. Jia, B. McCabe, Y. Chen, and J. Sun. 2019. “Impact of psychological capital on construction worker safety behavior: Communication competence as a mediator.” J. Saf. Res. 71 (Dec): 231–241. https://doi.org/10.1016/j.jsr.2019.09.007.
He, C., B. McCabe, G. Jia, and J. Sun. 2020. “Effects of safety climate and safety behavior on safety outcomes between supervisors and construction workers.” J. Constr. Eng. Manage. 146 (1): 04019092. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001735.
Hoyos, C. G., and F. Ruppert. 1995. “Safety diagnosis in industrial work settings: The safety diagnosis questionnaire.” J. Saf. Res. 26 (2): 107–117. https://doi.org/10.1016/0022-4375(95)00004-A.
Huang, Y., A. Hammad, and Z. Zhu. 2021. “Providing proximity alerts to workers on construction sites using Bluetooth Low Energy RTLS.” Autom. Constr. 132 (Dec): 103928. https://doi.org/10.1016/j.autcon.2021.103928.
Izadi Moud, H., I. Flood, X. Zhang, B. Abbasnejad, P. Rahgozar, and M. McIntyre. 2021. “Quantitative assessment of proximity risks associated with unmanned aerial vehicles in construction.” J. Manage. Eng. 37 (1): 04020095. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000852.
Jeelani, I., K. Asadi, H. Ramshankar, K. Han, and A. Albert. 2021. “Real-time vision-based worker localization & hazard detection for construction.” Autom. Constr. 121 (Jan): 103448. https://doi.org/10.1016/j.autcon.2020.103448.
Jeong, J., and J. Jeong. 2022. “Quantitative risk evaluation of fatal incidents in construction based on frequency and probability analysis.” J. Manage. Eng. 38 (2): 04021089. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000998.
Kalman, R. E. 1960. “A new approach to linear filtering and prediction problems.” J. Fluids Eng. Trans. ASME 82 (1): 35–45. https://doi.org/10.1115/1.3662552.
Kim, H., C. R. Ahn, and K. Yang. 2017. “Identifying safety hazards using collective bodily responses of workers.” J. Constr. Eng. Manage. 143 (2): 04016090. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001220.
Kim, H., K. Kim, and H. Kim. 2016. “Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects.” J. Comput. Civil Eng. 30 (4): 04015075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000562.
Kim, K., I. Jeong, and Y. K. Cho. 2023. “Signal processing and alert logic evaluation for IoT–based work zone proximity safety system.” J. Constr. Eng. Manage. 149 (2): 05022018. https://doi.org/10.1061/JCEMD4.COENG-12417.
Kong, T., W. Fang, P. E. D. Love, H. Luo, S. Xu, and H. Li. 2021. “Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction.” Adv. Eng. Inf. 50 (Oct): 101400. https://doi.org/10.1016/j.aei.2021.101400.
Kweon, Y.-J., and K. M. Kockelman. 2003. “Overall injury risk to different drivers: Combining exposure, frequency, and severity models.” Accid. Anal. Prev. 35 (4): 441–450. https://doi.org/10.1016/S0001-4575(02)00021-0.
Lee, K. P., H. S. Lee, M. Park, H. Kim, and S. Han. 2014. “A real-time location-based construction labor safety management system.” J. Civ. Eng. Manage. 20 (5): 724–736. https://doi.org/10.3846/13923730.2013.802728.
Li, H., X. Yang, M. Skitmore, F. Wang, and P. Forsythe. 2017. “Automated classification of construction site hazard zones by crowd-sourced integrated density maps.” Autom. Constr. 81 (Sep): 328–339. https://doi.org/10.1016/j.autcon.2017.04.007.
Lin, S. S., N. Zhang, A. Zhou, and S. L. Shen. 2022. “Risk evaluation of excavation based on fuzzy decision-making model.” Autom. Constr. 136 (Apr): 104143. https://doi.org/10.1016/j.autcon.2022.104143.
Liu, H. T., and Y. L. Tsai. 2012. “A fuzzy risk assessment approach for occupational hazards in the construction industry.” Saf. Sci. 50 (4): 1067–1078. https://doi.org/10.1016/j.ssci.2011.11.021.
Liu, W., J. Chang, and Y. Du. 2017. “Linguistic Heronian mean operators and applications in decision making.” Chin. J. Manage. Sci. 25 (4): 174–183. https://doi.org/10.16381/j.cnki.issn1003-207x.2017.04.021.
Luo, X., H. Li, T. Huang, and M. Skitmore. 2016. “Quantifying hazard exposure using real-time location data of construction workforce and equipment.” J. Constr. Eng. Manage. 142 (8): 040160311. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001139.
Man, S. S., A. H. S. Chan, and S. Alabdulkarim. 2019. “Quantification of risk perception: Development and validation of the construction worker risk perception (CoWoRP) scale.” J. Saf. Res. 71: 25–39. https://doi.org/10.1016/j.jsr.2019.09.009.
Ning, X., J. Qi, and C. Wu. 2018. “A quantitative safety risk assessment model for construction site layout planning.” Saf. Sci. 104: 246–259. https://doi.org/10.1016/j.ssci.2018.01.016.
People’s Republic of China National Standard. 2013. Unified code for technique for constructional safety. GB 50870-2013. Beijing: China Planning Press.
Pinto, A., I. L. Nunes, and R. A. Ribeiro. 2011. “Occupational risk assessment in construction industry—Overview and reflection.” Saf. Sci. 49 (5): 616–624. https://doi.org/10.1016/j.ssci.2011.01.003.
Rahman, M. M., L. Bobadilla, A. Mostafavi, T. Carmenate, and S. A. Zanlongo. 2018. “An automated methodology for worker path generation and safety assessment in construction projects.” IEEE Trans. Autom. Sci. Eng. 15 (2): 479–491. https://doi.org/10.1109/TASE.2016.2628898.
Rashid, K. M., and A. H. Behzadan. 2018. “Risk behavior-based trajectory prediction for construction site safety monitoring.” J. Constr. Eng. Manage. 144 (2): 04017106. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001420.
Razavi, S. N., and C. T. Haas. 2012. “Reliability-based hybrid data fusion method for adaptive location estimation in construction.” J. Comput. Civil Eng. 26 (1): 1–10. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000101.
Sacks, R., O. Rozenfeld, and Y. Rosenfeld. 2009. “Spatial and temporal exposure to safety hazards in construction.” J. Constr. Eng. Manage. 135 (8): 726–736. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:8(726).
Soltanmohammadlou, N., S. Sadeghi, C. K. H. Hon, and F. Mokhtarpour-Khanghah. 2019. “Real-time locating systems and safety in construction sites: A literature review.” Saf. Sci. 117 (Aug): 229–242. https://doi.org/10.1016/j.ssci.2019.04.025.
Teizer, J., and T. Cheng. 2015. “Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas.” Autom. Constr. 60 (Dec): 58–73. https://doi.org/10.1016/j.autcon.2015.09.003.
Teizer, J., U. Mantripragada, and M. Venugopal. 2008. “Analyzing the travel patterns of construction workers.” In Proc., 25th Int. Symp. on Automation and Robotics in Construction, 26–29. Vilnius, Lithuania: Technika.
Tzeng, G. H., and J. J. Huang. 1981. Multiple attribute decision making: Methods and applications. Berlin: Springer.
Wang, J., and S. Razavi. 2018. “Spatiotemporal network-based model for dynamic risk analysis on struck-by-equipment hazard.” J. Comput. Civil Eng. 32 (2): 04017089. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000732.
Wang, J., and S. N. Razavi. 2016. “Low False alarm rate model for unsafe-proximity detection in construction.” J. Comput. Civil Eng. 30 (2): 04015005. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000470.
Wu, S., L. Hou, G. Zhang, and H. Chen. 2022. “Real-time mixed reality-based visual warning for construction workforce safety.” Autom. Constr. 139 (Jun): 104252. https://doi.org/10.1016/j.autcon.2022.104252.
Xia, X., T. Zhou, J. Du, and N. Li. 2022. “Human motion prediction for intelligent construction: A review.” Autom. Constr. 142 (Oct): 104497. https://doi.org/10.1016/j.autcon.2022.104497.
Xiao, G., Z. Juan, and C. Zhang. 2015. “Travel mode detection based on GPS track data and Bayesian networks.” Comput. Environ. Urban Syst. 54 (Nov): 14–22. https://doi.org/10.1016/j.compenvurbsys.2015.05.005.
Xue, J., P. H. A. J. M. Van Gelder, G. Reniers, E. Papadimitriou, and C. Wu. 2019. “Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships’ maneuvering decisions using grey and fuzzy theories.” Saf. Sci. 120 (Dec): 323–340. https://doi.org/10.1016/j.ssci.2019.07.019.
Yang, K., C. R. Ahn, and H. Kim. 2018. “Tracking divergence in workers’ trajectory patterns for hazard sensing in construction.” In Proc., Construction Research Congress 2018: Safety and Disaster Management–Selected Papers from the Construction Research Congress 2018, 126–133. Reston, VA: ASCE.
Yang, X., X. Luo, H. Li, X. Luo, and H. Guo. 2017. “Location-based measurement and visualization for interdependence network on construction sites.” Adv. Eng. Inf. 34 (Oct): 36–45. https://doi.org/10.1016/j.aei.2017.09.003.
Zhang, L., X. Wu, M. J. Skibniewski, J. Zhong, and Y. Lu. 2014. “Bayesian-network-based safety risk analysis in construction projects.” Reliab. Eng. Syst. Saf. 131 (Nov): 29–39. https://doi.org/10.1016/j.ress.2014.06.006.
Zhang, M., and S. Ge. 2022. “Vision and trajectory–based dynamic collision prewarning mechanism for tower cranes.” J. Constr. Eng. Manage. 148 (7): 04022057. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002309.
Zheng, Y. 2015. “Trajectory data mining: An overview.” ACM Trans. Intell. Syst. Technol. 6 (3): 1–41. https://doi.org/10.1145/2743025.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 11 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 15, 2023
Accepted: Jul 7, 2023
Published online: Sep 12, 2023
Published in print: Nov 1, 2023
Discussion open until: Feb 12, 2024
ASCE Technical Topics:
- Business management
- Construction engineering
- Construction management
- Construction methods
- Construction sites
- Continuum mechanics
- Diagnosis
- Disaster risk management
- Dynamics (solid mechanics)
- Employment
- Engineering fundamentals
- Engineering mechanics
- Forensic engineering
- Information management
- Labor
- Motion (dynamics)
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Risk management
- Safety
- Solid mechanics
- Structural engineering
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