Dynamic Fall Risk Assessment Framework for Construction Workers Based on Dynamic Bayesian Network and Computer Vision
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 12
Abstract
Due to the dynamics of changing construction-related entities at construction sites and the hazardous work environment, safety accidents occur frequently, especially falls from heights. The current practice of fall risk assessment for construction workers, which mainly relies on manual observation by safety experts, is a static risk assessment that is time-consuming and laborious. A proactive, dynamic risk assessment framework is urgently needed to address this issue. In this work, computer vision has been combined with dynamic Bayesian network (DBN) to propose a dynamic risk assessment framework. The aim of the proposed framework is to improve the efficiency of risk assessment and reduce fall risk by automatically detecting onsite risk factor information. The proposed framework was tested using the activity of climbing ladders as a case study. The results show that the proposed dynamic fall risk assessment framework is feasible. It can be used to dynamically assess the fall risk of workers by automatically detecting the states of fall risk factors and capturing dynamic changes among the risk factors. The framework also includes a method of sending targeted early warnings to workers while assessing their risk levels, reducing the possibility of falls.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Al-Humaidi, H. M., and F. H. Tan. 2012. “Using fuzzy failure mode effect analysis to model cave-in accidents.” J. Perform. Constr. Facil. 26 (5): 702–719. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000276.
Aminbakhsh, S., M. Gunduz, and R. Sonmez. 2013. “Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects.” J. Saf. Res. 46 (Sep): 99–105. https://doi.org/10.1016/j.jsr.2013.05.003.
Arslan, M., C. Cruz, and D. Ginhac. 2019. “Visualizing intrusions in dynamic building environments for worker safety.” Saf. Sci. 120 (Dec): 428–446. https://doi.org/10.1016/j.ssci.2019.07.020.
Cai, B., Y. Liu, Z. Liu, X. Tian, X. Dong, and S. Yu. 2012. “Using Bayesian networks in reliability evaluation for subsea blowout preventer control system.” Reliab. Eng. Syst. Saf. 108 (Dec): 32–41. https://doi.org/10.1016/j.ress.2012.07.006.
Cai, B., Y. Liu, Y. Ma, Z. Liu, Y. Zhou, and J. Sun. 2015. “Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system.” ISA Trans. 58 (Sep): 595–604. https://doi.org/10.1016/j.isatra.2015.06.011.
Cakan, H., E. Kazan, and M. Usmen. 2014. “Investigation of factors contributing to fatal and nonfatal roofer fall accidents.” Int. J. Construct. Educ. Res. 10 (4): 300–317. https://doi.org/10.1080/15578771.2013.868843.
Cao, Z., G. Hidalgo, T. Simon, S.-E. Wei, and Y. Sheikh. 2018. “OpenPose: Realtime multi-person 2D pose estimation using Part Affinity Fields.” Preprint, submitted December 18, 2018. https://arxiv.org/abs/1812.08008.
Cha, Y. J., W. Choi, and O. Büyüköztürk. 2017. “Deep learning-based crack damage detection using convolutional neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
Chang, W.-T., C. J. Lin, Y.-H. Lee, and H.-J. Chen. 2018. “Development of an observational checklist for falling risk assessment of high-voltage transmission tower construction workers.” Int. J. Ind. Ergon. 68 (Nov): 73–81. https://doi.org/10.1016/j.ergon.2018.06.011.
Chen, C., Z. Zhu, and A. Hammad. 2020. “Automated excavators activity recognition and productivity analysis from construction site surveillance videos.” Autom. Constr. 110 (Feb): 103045. https://doi.org/10.1016/j.autcon.2019.103045.
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.
Chen, Y., Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun. 2018. “Cascaded pyramid network for multi-person pose estimation.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 7103–7112. New York: IEEE.
Cheng, B., B. Xiao, J. Wang, H. Shi, T. S. Huang, and L. Zhang. 2020. “Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 5386–5395. New York: IEEE.
Cheng, Y., B. Wang, B. Yang, and R. T. Tan. 2021. “Monocular 3D multi-person pose estimation by integrating top-down and bottom-up networks.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 7649–7659. New York: IEEE.
Choi, W., T. Isaka, H. Sekiguchi, and K. Hachimura. 2009. “Quantitative analysis of leg movement and EMG signal in expert Japanese traditional dancer.” In Advances in human-robot interaction, 165–178. New York: IEEE.
Ding, L., W. Fang, H. Luo, P. E. Love, B. Zhong, and X. Ouyang. 2018. “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory.” Autom. Constr. 86 (Feb): 118–124. https://doi.org/10.1016/j.autcon.2017.11.002.
Fang, Q., H. Li, X. Luo, L. Ding, T. M. Rose, W. An, and Y. Yu. 2018a. “A deep learning-based method for detecting non-certified work on construction sites.” Adv. Eng. Inf. 35 (Jan): 56–68. https://doi.org/10.1016/j.aei.2018.01.001.
Fang, W., L. Ding, H. Luo, and P. E. Love. 2018b. “Falls from heights: A computer vision-based approach for safety harness detection.” Autom. Constr. 91 (Jul): 53–61. https://doi.org/10.1016/j.autcon.2018.02.018.
Fang, W., B. Zhong, N. Zhao, P. E. Love, H. Luo, J. Xue, and S. Xu. 2019. “A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network.” Adv. Eng. Inf. 39 (Jan): 170–177. https://doi.org/10.1016/j.aei.2018.12.005.
Fang, Y., Y. K. Cho, and J. Chen. 2016. “A framework for real-time pro-active safety assistance for mobile crane lifting operations.” Autom. Constr. 72 (Dec): 367–379. https://doi.org/10.1016/j.autcon.2016.08.025.
Fu, S., D. Zhang, J. Montewka, X. Yan, and E. Zio. 2016. “Towards a probabilistic model for predicting ship besetting in ice in Arctic waters.” Reliab. Eng. Syst. Saf. 155 (Nov): 124–136. https://doi.org/10.1016/j.ress.2016.06.010.
Guanquan, C., and W. Jinhui. 2012. “Study on probability distribution of fire scenarios in risk assessment to emergency evacuation.” Reliab. Eng. Syst. Saf. 99 (Mar): 24–32. https://doi.org/10.1016/j.ress.2011.10.014.
Guo, H., Y. Yu, Q. Ding, and M. Skitmore. 2018. “Image-and-skeleton-based parameterized approach to real-time identification of construction workers’ unsafe behaviors.” J. Constr. Eng. Manage. 144 (6): 04018042. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001497.
Hallowell, M. R. 2012. “Safety-knowledge management in American construction organizations.” J. Manage. Eng. 28 (2): 203–211. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000067.
Hay, J. G. 1973. The center of gravity of the human body, 20–44. Washington, DC: Kinesiology III, American Association for Health, Physical Education, and Recreation.
Hu, Q., Y. Bai, L. He, Q. Cai, S. Tang, G. Ma, J. Tan, and B. Liang. 2020. “Intelligent framework for worker-machine safety assessment.” J. Constr. Eng. Manage. 146 (5): 04020045. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001801.
Jahangiri, M., H. R. J. Solukloei, and M. Kamalinia. 2019. “A neuro-fuzzy risk prediction methodology for falling from scaffold.” Saf. Sci. 117 (Aug): 88–99. https://doi.org/10.1016/j.ssci.2019.04.009.
Jebelli, H., C. R. Ahn, and T. L. Stentz. 2016. “Comprehensive fall-risk assessment of construction workers using inertial measurement units: Validation of the gait-stability metric to assess the fall risk of iron workers.” J. Comput. Civ. Eng. 30 (3): 04015034. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000511.
Jiang, H., P. Lin, Q. Fan, and M. Qiang. 2014. “Real-time safety risk assessment based on a real-time location system for hydropower construction sites.” Sci. World J. 2014:1–14. https://doi.org/10.1155/2014/235970.
Kang, Y. 2018. “Use of fall protection in the US construction industry.” J. Manage. Eng. 34 (6): 04018045. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000655.
Kang, Y., S. Siddiqui, S. J. Suk, S. Chi, and C. Kim. 2017. “Trends of fall accidents in the US construction industry.” J. Constr. Eng. Manage. 143 (8): 04017043. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001332.
Khan, B., F. Khan, B. Veitch, and M. Yang. 2018. “An operational risk analysis tool to analyze marine transportation in Arctic waters.” Reliab. Eng. Syst. Saf. 169 (Jan): 485–502. https://doi.org/10.1016/j.ress.2017.09.014.
Kim, H., K. Kim, and H. Kim. 2016a. “Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects.” J. Comput. Civ. Eng. 30 (4): 04015075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000562.
Kim, H., H.-S. Lee, M. Park, B. Chung, and S. Hwang. 2016b. “Automated hazardous area identification using laborers’ actual and optimal routes.” Autom. Constr. 65 (May): 21–32. https://doi.org/10.1016/j.autcon.2016.01.006.
Kolar, Z., H. Chen, and X. Luo. 2018. “Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images.” Autom. Constr. 89 (May): 58–70. https://doi.org/10.1016/j.autcon.2018.01.003.
Koulinas, G., P. Marhavilas, O. Demesouka, A. Vavatsikos, and D. Koulouriotis. 2019. “Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique—A case study for the Greek construction sector.” Saf. Sci. 112 (Feb): 96–104. https://doi.org/10.1016/j.ssci.2018.10.017.
Lee, S., D. W. Halpin, and H. Chang. 2006. “Quantifying effects of accidents by fuzzy-logic-and simulation-based analysis.” Can. J. Civ. Eng. 33 (3): 219–226. https://doi.org/10.1139/l05-026.
Lestari, R. I., B. H. Guo, and Y. M. Goh. 2019. “Causes, solutions, and adoption barriers of falls from roofs in the Singapore construction industry.” J. Constr. Eng. Manage. 145 (5): 04019027. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001649.
Leu, S.-S., and C.-M. Chang. 2015. “Bayesian-network-based fall risk evaluation of steel construction projects by fault tree transformation.” J. Civ. Eng. Manage. 21 (3): 334–342. https://doi.org/10.3846/13923730.2014.890643.
Lin, P., Q. Li, Q. Fan, X. Gao, and S. Hu. 2014. “A real-time location-based services system using WiFi fingerprinting algorithm for safety risk assessment of workers in tunnels.” Math. Probl. Eng. 2014: 1–10. https://doi.org/10.1155/2014/371456.
Liu, Q., A. Tchangani, and F. Pérès. 2016. “Modelling complex large scale systems using object oriented Bayesian networks (OOBN).” IFAC-PapersOnLine 49 (12): 127–132. https://doi.org/10.1016/j.ifacol.2016.07.562.
Luo, Z., L. Zeng, H. Pan, Q. Hu, B. Liang, and J. Han. 2019. “Research on construction safety risk assessment of new subway station close-attached undercrossing the existing operating station.” Math. Probl. Eng. 2019:1–20. https://doi.org/10.1155/2019/3215219.
Matías, J., T. Rivas, C. Ordóñez, and J. Taboada. 2007. “Assessing the environmental impact of slate quarrying using Bayesian networks and GIS.” In Proc., AIP Conf. Proc., 1285–1288. College Park, MD: American Institute of Physics.
Meng, W.-L., S. Shen, and A. Zhou. 2018. “Investigation on fatal accidents in Chinese construction industry between 2004 and 2016.” Nat. Hazards 94 (2): 655–670. https://doi.org/10.1007/s11069-018-3411-z.
Mistikoglu, G., I. H. Gerek, E. Erdis, P. M. Usmen, H. Cakan, and E. E. Kazan. 2015. “Decision tree analysis of construction fall accidents involving roofers.” Expert Syst. Appl. 42 (4): 2256–2263. https://doi.org/10.1016/j.eswa.2014.10.009.
Mneymneh, B. E., M. Abbas, and H. Khoury. 2019. “Vision-based framework for intelligent monitoring of hardhat wearing on construction sites.” J. Comput. Civ. Eng. 33 (2): 04018066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000813.
Mohandes, S. R., H. Sadeghi, A. Mahdiyar, S. Durdyev, A. Banaitis, K. Yahya, and S. Ismail. 2020. “Assessing construction labours’ safety level: A fuzzy MCDM approach.” J. Civ. Eng. Manage. 26 (2): 175–188. https://doi.org/10.3846/jcem.2020.11926.
Murphy, K., A. Torralba, D. Eaton, and W. Freeman. 2006. “Object detection and localization using local and global features.” In Toward category-level object recognition, 382–400. Berlin: Springer.
Newell, A., K. Yang, and J. Deng. 2016. “Stacked hourglass networks for human pose estimation.” In Proc., European Conf. on Computer Vision, 483–499. Cham, Switzerland: Springer.
Nguyen, L. D., D. Q. Tran, and M. P. Chandrawinata. 2016. “Predicting safety risk of working at heights using Bayesian networks.” J. Constr. Eng. Manage. 142 (9): 04016041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001154.
Omidvari, M., S. M. Abootorabi, and H. Mehrno. 2016. “An investigation of the influence of managerial factors on industrial accidents in the construction industry using the gray FTA method.” In Grey Syst. Theory Appl. 6 (1): 96–109. https://doi.org/10.1108/GS-01-2016-0001.
Pinto, A. 2014. “QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets.” Saf. Sci. 63 (Mar): 57–76. https://doi.org/10.1016/j.ssci.2013.10.019.
Qian, H., J. Xu, and J. Zhou. 2018. “Object detection using deep convolutional neural networks.” In Proc., 2018 Chinese Automation Congress (CAC), 1151–1156. New York: IEEE.
Qian, H., R. Zhang, and Y.-J. Zhang. 2020. “Dynamic risk assessment of natural environment based on Dynamic Bayesian Network for key nodes of the arctic Northwest Passage.” Ocean Eng. 203 (May): 107205. https://doi.org/10.1016/j.oceaneng.2020.107205.
Ren, S., K. He, R. Girshick, and J. Sun. 2015. “Faster R-CNN: Towards real-time object detection with region proposal networks.” In Proc., Advances in Neural Information Processing Systems, 91–99. La Jolla, CA: Neural Information Processing Systems.
Seo, J., S. Han, S. Lee, and H. Kim. 2015. “Computer vision techniques for construction safety and health monitoring.” Adv. Eng. Inf. 29 (2): 239–251. https://doi.org/10.1016/j.aei.2015.02.001.
Shim, C.-S., K.-M. Lee, L. S. Kang, J. Hwang, and Y. Kim. 2012. “Three-dimensional information model-based bridge engineering in Korea.” Struct. Eng. Int. 22 (1): 8–13. https://doi.org/10.2749/101686612X13216060212834.
Shokouhi, Y., P. Nassiri, I. Mohammadfam, and K. Azam. 2019. “Predicting the probability of occupational fall incidents: A Bayesian network model for the oil industry.” Int. J. Occup. Saf. Ergon. 27 (3): 654–663. https://doi.org/10.1080/10803548.2019.1607052.
Sousa, V., N. M. Almeida, and L. A. Dias. 2014. “Risk-based management of occupational safety and health in the construction industry—Part 1: Background knowledge.” Saf. Sci. 66 (Jul): 75–86. https://doi.org/10.1016/j.ssci.2014.02.008.
Teizer, J. 2015. “Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites.” Adv. Eng. Inf. 29 (2): 225–238. https://doi.org/10.1016/j.aei.2015.03.006.
Uchoa, J. G. L., M. J. A. de Sousa, L. H. V. Silva, and A. L. D. O. Cavaignac. 2019. “FMEA method application based on occupational risks in the construction industry on work at height: A theoretical contribution.” Int. J. Adv. Eng. Res. Sci. 6 (10): 261–278. https://doi.org/10.22161/ijaers.610.40.
Wang, J., and S. Razavi. 2017. “A comprehensive spatio-temporal network-based model for dynamic risk analysis on struck-by-equipment hazard.” In Proc., Computing in Civil Engineering 2017, 384–391. Reston, VA: ASCE.
Warner, K. G., and R. H. Demling. 1986. “The pathophysiology of free-fall injury.” Ann. Emergency Med. 15 (9): 1088–1093. https://doi.org/10.1016/S0196-0644(86)80134-2.
Wei, S.-E., V. Ramakrishna, T. Kanade, and Y. Sheikh. 2016. “Convolutional pose machines.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 4724–4732. New York: IEEE.
Wu, S., L. Zhang, W. Zheng, Y. Liu, and M. A. Lundteigen. 2016. “A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents.” J. Nat. Gas Sci. Eng. 34 (Aug): 139–158. https://doi.org/10.1016/j.jngse.2016.06.054.
Xu, W., and T.-K. Wang. 2020. “Dynamic safety prewarning mechanism of human-machine-environment using computer vision.” Eng. Constr. Archit. Manage. 27 (8): 1813–1833. https://doi.org/10.1108/ECAM-12-2019-0732.
Xue, Y., and Y. Li. 2018. “A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects.” Comput.-Aided Civ. Infrastruct. Eng. 33 (8): 638–654. https://doi.org/10.1111/mice.12367.
Yan, X., X. Li, Y. Liu, and J. Zhao. 2014. “Effects of foggy conditions on drivers’ speed control behaviors at different risk levels.” Saf. Sci. 68 (Oct): 275–287. https://doi.org/10.1016/j.ssci.2014.04.013.
Yan, X., H. Zhang, and H. Li. 2020. “Computer vision-based recognition of 3D relationship between construction entities for monitoring struck-by accidents.” Comput.-Aided Civ. Infrastruct. Eng. 35 (9): 1023–1038. https://doi.org/10.1111/mice.12536.
Zhou, Y., N. Fenton, and M. Neil. 2014. “Bayesian network approach to multinomial parameter learning using data and expert judgments.” Int. J. Approximate Reason. 55 (5): 1252–1268. https://doi.org/10.1016/j.ijar.2014.02.008.
Zhou, Y., W. Su, L. Ding, H. Luo, and P. E. Love. 2017. “Predicting safety risks in deep foundation pits in subway infrastructure projects: Support vector machine approach.” J. Comput. Civ. Eng. 31 (5): 04017052. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000700.
Zlatar, T., E. M. G. Lago, W. D. A. Soares, J. D. S. Baptista, and B. Barkokébas Junior. 2019. “Falls from height: Analysis of 114 cases.” Production 29: e20180091. https://doi.org/10.1590/0103-6513.20180091.
Zou, P. X., and R. Y. Sunindijo. 2015. Strategic safety management in construction and engineering. New York: Wiley.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 29, 2020
Accepted: Aug 19, 2021
Published online: Sep 29, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 28, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Xianbo Zhao, Construction risk management research: intellectual structure and emerging themes, International Journal of Construction Management, 10.1080/15623599.2023.2167303, (1-11), (2023).
- Fatemeh Mostofi, Vedat Toğan, A Data-Driven Recommendation System for Construction Safety Risk Assessment, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13437, 149, 12, (2023).
- Wenpei Xu, Ting-Kwei Wang, Construction Worker Safety Prediction and Active Warning Based on Computer Vision and the Gray Absolute Decision Analysis Method, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-12695, 149, 4, (2023).
- Hunsang Choo, Bogyeong Lee, Hyunsoo Kim, Byungjoo Choi, Automated detection of construction work at heights and deployment of safety hooks using IMU with a barometer, Automation in Construction, 10.1016/j.autcon.2022.104714, 147, (104714), (2023).
- Mengwei Ye, Junwu Wang, Xiang Si, Shiman Zhao, Qiyun Huang, Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN, Sustainability, 10.3390/su14031864, 14, 3, (1864), (2022).
- Mei Liu, Boning Li, Hongjun Cui, Pin-Chao Liao, Yuecheng Huang, Research Paradigm of Network Approaches in Construction Safety and Occupational Health, International Journal of Environmental Research and Public Health, 10.3390/ijerph191912241, 19, 19, (12241), (2022).
- Vedat Toğan, Fatemeh Mostofi, Yunus Emre Ayözen, Onur Behzat Tokdemir, Customized AutoML: An Automated Machine Learning System for Predicting Severity of Construction Accidents, Buildings, 10.3390/buildings12111933, 12, 11, (1933), (2022).
- Sharjeel Anjum, Numan Khan, Rabia Khalid, Muhammad Khan, Dongmin Lee, Chansik Park, Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method, IEEE Access, 10.1109/ACCESS.2022.3164676, 10, (36725-36742), (2022).
- Zhipeng Zhou, Song Liu, Haonan Qi, Mitigating subway construction collapse risk using Bayesian network modeling, Automation in Construction, 10.1016/j.autcon.2022.104541, 143, (104541), (2022).