Share:


A real-time control approach based on intelligent video surveillance for violations by construction workers

    Shengyu GUO Affiliation
    ; Chaohua XIONG Affiliation
    ; Peisong GONG Affiliation

Abstract

The unsafe behavior of workers is the main object of construction safety management, in which violations require increased attention due to their pernicious consequences. However, existing studies have merely discussed violations separately from unsafe behaviors. To respond quickly to workers’ violations on site, this study proposes a real-time control approach based on intelligent video surveillance. First, scenes reflecting unsafe behaviors are automatically acquired through camera-based behavior analysis technology. Meanwhile, the time corresponding to the construction phase is recorded. Second, the temporal association rule model of worker’s unsafe behavior is constructed, and the rule “construction phase→unsafe behavior” is determined by the Apriori algorithm to identify target behaviors necessary for critical control in different construction phases. Finally, statistical process control is used to find the trends of violations with frequency and mass characteristics through the dynamic monitoring of target behavior. In addition, real-time alerts of these unsafe acts are produced simultaneously. A pilot study is conducted on the cross-river tunnel project in Wuhan city, Hubei, China, and the violations related to construction machineries is proven to be controllable. Thus, the proposed approach promotes behavioral safety management on construction since it effectively controls workers’ violations by real-time monitoring and analysis.

Keyword : unsafe behavior, violation, association rule, statistical process control, intelligent video surveillance

How to Cite
GUO, S., XIONG, C., & GONG, P. (2018). A real-time control approach based on intelligent video surveillance for violations by construction workers. Journal of Civil Engineering and Management, 24(1), 67-78. https://doi.org/10.3846/jcem.2018.301
Published
Mar 9, 2018
Abstract Views
53
PDF Downloads
47
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Agrawal, R.; Mannila, H.; Srikant, R.; Toivonen, H.; Verkamo, A. I. 1996. Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining 12(1): 307–328.

Aguilar, G. E.; Hewage, K. N. 2013. IT based system for construction safety management and monitoring: C-RTICS2, Automation in Construction 35: 217–228. https://doi.org/10.1016/j.autcon.2013.05.007

Aliverdi, R.; Moslemi Naeni, L.; Salehipour, A. 2013. Monitoring project duration and cost in a construction project by applying statistical quality control charts, International Journal of Project Management 31(3): 411–423. https://doi.org/10.1016/j.ijproman.2012.08.005

Alper, S. J.; Karsh, B. T. 2009. A systematic review of safety violations in industry, Accident Analysis & Prevention 41(4): 739–754. https://doi.org/10.1016/j.aap.2009.03.013

Barabasi, A.-L. 2005. The origin of bursts and heavy tails in human dynamics, Nature 435: 207–211. https://doi.org/10.1038/nature03459

Cavazza, N.; Serpe, A. 2009. Effects of safety climate on safety norm violations: exploring the mediating role of attitudinal ambivalence toward personal protective equipment, Journal of Safety Research 40(4): 277–283. https://doi.org/10.1016/j.jsr.2009.06.002

Cheng, C.-W.; Lin, C.-C.; Leu, S.-S. 2010. Use of association rules to explore cause–effect relationships in occupational accidents in the Taiwan construction industry, Safety Science 48(4): 436–444. https://doi.org/10.1016/j.ssci.2009.12.005

Cheng, Y.; Yu, W. D.; Li, Q. 2015. GA-based multi-level association rule mining approach for defect analysis in the construction industry, Automation in Construction 51: 78–91. https://doi.org/10.1016/j.autcon.2014.12.016

Choudhry, R. M. 2014. Behavior-based safety on construction sites: A case study, Accident Analysis.& Prevention 70: 14–23. https://doi.org/10.1016/j.aap.2014.03.007

Cigularov, K. P.; Chen, P. Y.; Rosecrance, J. 2010. The effects of error management climate and safety communication on safety: A multi-level study, Accident Analysis & Prevention 42(5): 1498–1506. https://doi.org/10.1016/j.aap.2010.01.003

DeJoy, D. M. 2005. Behavior change versus culture change: Divergent approaches to managing workplace safety, Safety Science 43(2): 105–129. https://doi.org/10.1016/j.ssci.2005.02.001

EIA557B Statistical Process Control Systems. Systems Management Council, 2006.

GB/T 4091-2001 Standard of Shewhart Control Charts. The General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, The Standardization Administration of the People’s Republic of China, 2001.

Geller, E. S. 2011. Psychological science and safety large-scale success at preventing occupational injuries and fatalities, Current Directions in Psychological Science 20(2): 109–114. https://doi.org/10.1177/0963721411402667

Guo, H.; Yu, Y.; Skitmore, M. 2017. Visualization technology-based construction safety management: A review, Automation in Construction 73: 135–144. https://doi.org/10.1016/j.autcon.2016.10.004

Guo, S. Y.; Ding, L. Y.; Luo, H. B.; Jiang, X. Y. 2016. A Big-Data-based platform of workers’ behavior: Observations from the field, Accident Analysis & Prevention 93: 299–309. https://doi.org/10.1016/j.aap.2015.09.024

Han, S.; Lee, S. 2013. A vision-based motion capture and recognition framework for behavior-based safety management, Automation in Construction 35: 131–141. https://doi.org/10.1016/j.autcon.2013.05.001

Heinrich, H. W.; Petersen, D.; Roos, N. 1980. Industrial accident prevention. New York: McGraw-Hill.

Helmreich, R. L. 2000. On error management: lessons from aviation, BMJ: British Medical Journal 320: 781.

Isaac, S.; Edrei, T. 2016. A statistical model for dynamic safety risk control on construction sites, Automation in Construction 63: 66–78. https://doi.org/10.1016/j.autcon.2015.12.006

Jazayeri, E.; Dadi, G. B. 2017. Construction safety management systems and methods of safety performance measurement: A review, Journal of Safety Engineering 6(2): 15–28.

Li, H.; Lu, M.; Hsu, S. C.; Gray, M.; Huang, T. 2015. Proactive behavior-based safety management for construction safety improvement, Safety Science 75: 107–117. https://doi.org/10.1016/j.ssci.2015.01.013

Liao, C.-W.; Perng, Y.-H. 2008. Data mining for occupational injuries in the Taiwan construction industry, Safety Science 46(7): 1091–1102. https://doi.org/10.1016/j.ssci.2007.04.007

Lingard, H.; Pink, S.; Hayes, J.; McDermott, V. 2016. Using participatory video to understand subcontracted construction workers’ safety rule violations, in Proceedings of the 32nd Annual ARCOM Conference: Construction Work and the Worker. 5–7 September 2016, Manchester, UK, Association of Researchers in Construction Management, Vol. 1, 457–466.

Liu, H.; Jazayeri, E.; Dadi, G. B. 2017. Establishing the influence of owner practices on construction safety in an operational excellence model, Journal of Construction Engineering and Management 143(6): 04017005. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0001292

Liu, H.; Jazayeri, E.; Dadi, G. B.; Maloney, W. F.; Cravey, K. J. 2015. Development of an operational excellence model to improve safety for construction organizations, in Proceedings of ICSC15: The Canadian Society for Civil Engineering 5th International/11th Construction Specialty Conference, 2015, University of British Columbia, Vancouver, Canada. Vol. 96, 1–10. https://doi.org/10.14288/1.0076355

Liu, Z. 2016. Safety management analysis for construction industry: Statistical Process Control (SPC) approach: Thesis. The Faculty of the College of Business and Technology, Morehead State University.

Love, P. E.; Smith, J. 2016. Error management: implications for construction, Construction Innovation 16(4): 418–424. http://www.emeraldinsight.com/doi/abs/10.1108/CI-01-2016-0001

McSween, T. E. 2003. Values-based safety process: Improving your safety culture with behavior-based safety. New York: John Wiley & Sons. https://doi.org/10.1002/0471721611

Montgomery, D. C. 2009. Introduction to statistical quality control. New York: John Wiley & Sons.

Ni, H.; Chen, A.; Chen, N. 2010. Some extensions on risk matrix approach, Safety Science 48(10): 1269–1278. https://doi.org/10.1016/j.ssci.2010.04.005

O’Dea, A.; Flin, R. 2001. Site managers and safety leadership in the offshore oil and gas industry, Safety Science 37(1): 39–57. https://doi.org/10.1016/S0925-7535(00)00049-7

Oakland, J. S. 2007. Statistical process control. Routledge.

Paul, R.; Garvey, P. R.; Lansdowne, Z. F. 1998. Risk matrix: an approach for identifying, assessing, and ranking program risks, Air Force Journal of Logistics 22(1): 16–23.

Reason, J. T. 1997. Managing the risks of organizational accidents. Ashgate Aldershot.

Sacks, R.; Perlman, A.; Barak, R. 2013. Construction safety training using immersive virtual reality, Construction Management and Economics 31(9): 1005–1017. http://dx.doi.org/10.1080/01446193.2013.828844

Sarawagi, S.; Thomas, S.; Agrawal, R. 2000. Integrating association rule mining with relational database systems: Alternatives and implications, Data Mining and Knowledge Discovery 4(2): 89–125. http://dx.doi.org/10.1023/a:1009887712954

Shewhart, W. A. 1931. Economic control of quality of manufactured products. New York: Macmillan.

Shrestha, K.; Shrestha, P. P.; Bajracharya, D.; Yfantis, E. A. 2015. Hard-hat detection for construction safety visualization, Journal of Construction Engineering, Article ID 721380. http://dx.doi.org/10.1155/2015/721380

Skinner, B. F. 1953. Science and human behavior. Simon and Schuster.

Stewart, R. A.; Spencer, C. A. 2006. Six-sigma as a strategy for process improvement on construction projects: a case study, Construction Management and Economics 24(4): 339–348. http://dx.doi.org/10.1080/01446190500521082

Teizer, J.; Cheng, T.; Fang, Y. 2013. Location tracking and data visualization technology to advance construction ironworkers’ education and training in safety and productivity, Automation in Construction 35: 53–68. https://doi.org/10.1016/j.autcon.2013.03.004

Verma, A.; Khan, S. D.; Maiti, J.; Krishna, O. B. 2014. Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports, Safety Science 70: 89–98. https://doi.org/10.1016/j.ssci.2014.05.007

Wetzel, E. M.; Thabet, W. Y. 2016. Utilizing Six Sigma to develop standard attributes for a safety for facilities management (SFFM) framework, Safety Science 89: 355–368. https://doi.org/10.1016/j.ssci.2016.07.010

Zhang, M. Z.; Fang, D. P. 2013. A continuous behavior-based safety strategy for persistent safety improvement in construction industry, Automation in Construction 34: 101–107. https://doi.org/10.1016/j.autcon.2012.10.019

Zhou, J. 2010. SPA–fuzzy method based real-time risk assessment for major hazard installations storing flammable gas, Safety Science 48(6): 819–822. https://doi.org/10.1016/j.ssci.2010.02.012

Zhu, Z.; Park, M.-W.; Koch, C.; Soltani, M.; Hammad, A.; Davari, K. 2016. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety, Automation in Construction 68: 95–101. https://doi.org/10.1016/j.autcon.2016.04.009

Zohar, D. 2014. Safety climate: Conceptualization, measurement, and improvement. The Oxford handbook of organizational climate and culture.

Send mail to Author


Send Cancel