Intelligent Framework for Worker-Machine Safety Assessment
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 5
Abstract
Intelligent safety management based on machine vision has become indispensable in reducing collision safety accidents during construction. To prevent collisions between workers and machines in excavation site construction, a real-time intelligent evaluation system to reflect worker–machine safety status was developed. The system included: (1) determination of the key factors affecting the safety of the interactive operation between workers and machines; (2) extraction of precursor semantic information related to the safety assessment for each object in the construction site based on machine vision; and (3) assessment of the safety state of a monitored object using a fuzzy neural network. A case study of excavation site construction is presented to illustrate and verify the entire process of safety assessment using the developed framework. The results show that the proposed model achieves high detection rates: 96% and 94% for tracking accuracy and 91.67% for prediction accuracy.
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Data Availability Statement
All data generated or analyzed during the study are included in the published paper. Information about the journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The first two authors contributed equally to this work. The system was developed by Yu Bai, under the supervision of Qijun Hu and Leping He. Experiment planning and setup were conducted by Qijie Cai and Guoli Ma. Shuang Tang, Jie Tan, and Baowei Liang provided valuable insight in preparing this manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 51574201), Research and Innovation Team of Provincial Universities in Sichuan (18TD0014), and Excellent Youth Foundation of the Sichuan Scientific Committee (2019JDJQ0037).
References
Aengchuan, P., and B. Phruksaphanrat. 2018. “Comparison of fuzzy inference system (FIS), FIS with artificial neural networks () and FIS with adaptive neuro-fuzzy inference system () for inventory control.” J. Intell. Manuf. 29 (4): 905–923. https://doi.org/10.1007/s10845-015-1146-1.
Bradski, G., and A. Daebler. 2009. Learning OpenCV. Computer vision with OpenCV library. Sebastopol, CA: O’Reilly Media.
Cheng, C. F., A. Rashidi, M. A. Davenport, and D. V. Anderson. 2017. “Activity analysis of construction equipment using audio signals and support vector machines.” Autom. Constr. 81 (Sep): 240–253. https://doi.org/10.1016/j.autcon.2017.06.005.
Chinese Standard. 2011. Standard of construction safety inspection. JGJ592011. Beijing: China Building Industry Press.
Chinese Standard. 2012. Technical regulations for building foundation pit support. JGJ 120201. Beijing: China Building Industry Press.
Chi, S., and C. H. Caldas. 2012. “Image-based safety assessment: Automated spatial safety risk identification of earthmoving and surface mining activities.” J. Constr. Eng. Manage. 138 (3): 341. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000438.
Ding, L. Y., and C. Zhou. 2013. “Development of web-based system for safety risk early warning in urban metro construction.” Autom. Constr. 34 (13): 45–55. https://doi.org/10.1016/j.autcon.2012.11.001.
Ding, L. Y., C. Zhou, Q. X. Deng, H. B. Luo, X. W. Ye, Y. Q. Ni, and P. Guo. 2013. “Real-time safety early warning system for cross passage construction in Yangtze Riverbed Metro Tunnel based on the internet of things.” Autom. Constr. 36 (Dec): 25–37. https://doi.org/10.1016/j.autcon.2013.08.017.
Elwakil, E., and T. Zayed. 2015. “Construction knowledge discovery system using fuzzy approach.” Can. J. Civ. Eng. 42 (1): 22–32. https://doi.org/10.1139/cjce-2014-0153.
Fang, W., L. Ding, B. Zhong, P. E. D. Love, and H. Luo. 2018. “Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach.” Adv. Eng. Inf. 37 (Aug): 139–149. https://doi.org/10.1016/j.aei.2018.05.003.
Han, J. Q. 2009. Machine vision technology and application. Beijing: Higher Education Press.
Han, S., and S. Lee. 2013. “A vision-based motion capture and recognition framework for behavior-based safety management.” Autom. Constr. 35 (Nov): 131–141. https://doi.org/10.1016/j.autcon.2013.05.001.
Hinze, J. W., and J. Teizer. 2011. “Visibility-related fatalities related to construction equipment.” Saf. Sci. 49 (5): 709–718. https://doi.org/10.1016/j.ssci.2011.01.007.
Izumi, C., S. Akutagawa, J. Tyagi, R. Abe, C. R. Sekhar, and A. Kusui. 2014. “Application of a new monitoring scheme “On Site Visualization” for safety management on Delhi Metro project.” Tunnelling Underground Space Technol. 44 (44): 130–147. https://doi.org/10.1016/j.tust.2014.07.012.
Jinpeng, H., M. Fangwu, and L. Weiguo. 2014. “Comparative test between ISO15623 on forward vehicle collision warning system and NHTSA 26555 assessment program.” [In Chinese.] Automob. Technol. 7: 28–33. https://doi.org/10.3969/j.issn.1000-3703.2014.07.008.
Kanan, R., O. Elhassan, and R. Bensalem. 2018. “An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies.” Autom. Constr. 88 (Apr): 73–86. https://doi.org/10.1016/j.autcon.2017.12.033.
Kazan, E., and M. A. Usmen. 2018. “Worker safety and injury severity analysis of earthmoving equipment accidents.” J. Saf. Res. 65 (Jun): 73–81. https://doi.org/10.1016/j.jsr.2018.02.008.
Kim, H., C. Kim, H. Jeong, B. Elhamim, and H. Kim. 2014. “On-site safety management using image processing and fuzzy inference.” In Proc., Int. Conf. on Computing in Civil and Building Engineering. Orlando, FL: Computing in Civil and Building Engineering.
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. Civ. Eng. 30 (4): 04015075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000562.
Kim, J., S. Chi, and J. Seo. 2018. “Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks.” Autom. Constr. 87 (Mar): 297–308. https://doi.org/10.1016/j.autcon.2017.12.016.
Li, H., M. Lu, S. C. Hsu, M. Gray, and T. Huang. 2015. “Proactive behavior-based safety management for construction safety improvement.” Saf. Sci. 75 (6): 107–117. https://doi.org/10.1016/j.ssci.2015.01.013.
Locke, E. A. 1968. “Toward a theory of task motivation and incentives.” Organ. Behav. Hum. Perform. 3 (2): 157–189. https://doi.org/10.1016/0030-5073(68)90004-4.
Lu, Q., S. Lee, and L. Chen. 2018. “Image-driven fuzzy-based system to construct as-is IFC BIM objects.” Autom. Constr. 92 (Aug): 68–87. https://doi.org/10.1016/j.autcon.2018.03.034.
Luo, H., C. Xiong, W. Fang, P. E. D. Love, B. Zhang, and X. Ouyang. 2018. “Convolutional neural networks: Computer vision-based workforce activity assessment in construction.” Autom. Constr. 94 (Oct): 282–289. https://doi.org/10.1016/j.autcon.2018.06.007.
Patel, D. A., and K. N. Jha. 2016. “Evaluation of construction projects based on the safe work behavior of co-employees through a neural network model.” Saf. Sci. 89 (Nov): 240–248. https://doi.org/10.1016/j.ssci.2016.06.020.
Perlman, A., R. Sacks, and R. Barak. 2014. “Hazard recognition and risk perception in construction.” Saf. Sci. 64 (Apr): 22–31. https://doi.org/10.1016/j.ssci.2013.11.019.
Pradhananga, N., and J. Teizer. 2013. “Automatic spatio-temporal analysis of construction site equipment operations using GPS data.” Autom. Constr. 29 (Jan): 107–122. https://doi.org/10.1016/j.autcon.2012.09.004.
Schlechtingen, M., I. F. Santos, and S. Achiche. 2013. “Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description.” Appl. Soft Comput. J. 13 (1): 259–270. https://doi.org/10.1016/j.asoc.2012.08.033.
Shuliang, Z., Y. Tao, and L. Jun. 2018. “Traffic safety early warning at intersection based on machine vision and information sharing.” [In Chinese.] J. Automot. Saf. Energy 9 (2): 156–163. https://doi.org/10.3969/j.issn.1674-8484.2018.02.005.
Siminski, K. 2017. “Interval type-2 neuro-fuzzy system with implication-based inference mechanism.” Expert Syst. Appl. 79 (Aug): 140–152. https://doi.org/10.1016/j.eswa.2017.02.046.
Stauffer, C., and W. Grimson. 1999. “Adaptive background mixture models for real-time tracking.” In Vol. 2 of Proc., 1999 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (Cat. No. PR00149), 246–252. New York: IEEE.
Teizer, J., B. S. Allread, C. E. Fullerton, and J. Hinze. 2010. “Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system.” Autom. Constr. 19 (5): 630–640. https://doi.org/10.1016/j.autcon.2010.02.009.
Valipour, A., N. Yahaya, N. M. Noor, J. Antucheviä Ienä, and J. Tamoå Aitienä. 2017. “Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study.” J. Civ. Eng. Manage. 23 (4): 524–532. https://doi.org/10.3846/13923730.2017.1281842.
Wu, W., H. Yang, D. A. S. Chew, S. H. Yang, A. G. F. Gibb, and Q. Li. 2010. “Towards an autonomous real-time tracking system of near-miss accidents on construction sites.” Autom. Constr. 19 (2): 134–141. https://doi.org/10.1016/j.autcon.2009.11.017.
Wu, Y., J. Lim, and M. Yang. 2013. “Online object tracking: A benchmark.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). New York: IEEE.
Xin, N., J. Qi, and C. Wu. 2018. “A quantitative safety risk assessment model for construction site layout planning.” Saf. Sci. 104 (Apr): 246–259. https://doi.org/10.1016/j.ssci.2018.01.016.
Yang, M., J. Wang, and T. Sun. 2018. “EMD-based preprocessing with a fuzzy inference system and a fuzzy neural network to identify kiln coating collapse for predicting refractory failure in the cement process.” Int. J. Fuzzy Syst. 20 (8): 2640–2656. https://doi.org/10.1007/s40815-018-0510-7.
Zhang, M., and D. Fang. 2013. “A continuous behavior-based safety strategy for persistent safety improvement in construction industry.” Automat. Constr. 34 (Sep): 101–107. https://doi.org/10.1016/j.autcon.2012.10.019.
Zhang, P., N. Li, D. Fang, and H. Wu. 2017. “Supervisor-focused behavior-based safety method for the construction industry: Case study in Hong Kong.” J. Constr. Eng. Manage. 143 (7): 05017009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001294.
Zhou, C., and L. Y. Ding. 2017. “Safety barrier warning system for underground construction sites using Internet-of-Things technologies.” Autom. Constr. 83 (Nov): 372–389. https://doi.org/10.1016/j.autcon.2017.07.005.
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 27, 2019
Accepted: Sep 24, 2019
Published online: Mar 11, 2020
Published in print: May 1, 2020
Discussion open until: Aug 11, 2020
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