Abstract
As the information technology and its application level improve, the construction industry is transforming and upgrading from traditional construction to smart construction in recent years. However, safety issues still exist and construction accidents occur frequently. Construction workers’ unsafe behaviors are the primary reason for accidents; how to reduce workers’ unsafe behaviors by conducting safety management is an urgent and crucial subject. The study considered the intelligent video surveillance and five traditional management modes into the management system and proposed an advanced cognitive model of construction workers’ unsafe behaviors. The agent-based modeling method was adopted to explore the action mechanisms of typical management factors on the cognitive attributes of workers’ unsafe behaviors. A case study was undertaken based on a prefabricated residential building construction project, and the theoretical model was verified by comparison with empirical data. Sensitivity analysis was conducted to determine the relative importance of different management factors. The results indicate that managers’ positive demonstration roles and behavior feedback have the best performance on preventing the spread of unsafe behaviors; daily meeting, appropriate frequency of communication, and all-directional intelligent video surveillance can effectively reduce unsafe behaviors, while frequent safety inspection has little effect on improving the safety performance. Connecting with the present development situation of construction industry, these findings are beneficial as an academic reference for transforming construction enterprises to formulate effective safety management strategies and prevent accidents.
Similar content being viewed by others
References
Allan M, Jones-Otazo H, Richardson GM (2009) Inhalation rates for risk assessments involving construction workers in Canada. Hum Ecol Risk Assess 15(2):371–387. https://doi.org/10.1080/10807030902761445
Axelrod R (1997) Advancing the art of simulation in the social sciences. Complexity 3(2):16–22. https://doi.org/10.1002/(SICI)1099-0526(199711/12)3:2%3c16::AID-CPLX4%3e3.0.CO
Becker D, Schaufele B, Einsiedler J, Sawade O, Radusch I (2014) Vehicle and pedestrian collision prevention system based on smart video surveillance and C2I communication. In: Proceeding of the 17th international IEEE conference on intelligent transportation systems (ITSC), October 08–11, Qingdao, China
Chang YHJ, Mosleh A (2007) Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model. Reliab Eng Syst Saf 92(8):1014–1040. https://doi.org/10.1016/j.ress.2006.05.010
Chen PY, Sampson JM, Dearmond S (2014) Role of safety stressors and social support on safety performance. Saf Sci 64(3):137–145. https://doi.org/10.1016/j.ssci.2013.11.025
Chmutina K, Rose J (2018) Building resilience: knowledge, experience and perceptions among informal construction stakeholders. Int J Disaster Risk Reduct 28:158–164. https://doi.org/10.1016/j.ijdrr.2018.02.039
Choi B, Lee SH (2018) An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior. J Constr Eng Manag 144(2):04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421
Cigularov KP, Chen PY, Rosecrance J (2010) The effects of error management climate and safety communication on safety: a multi-level study. Accid Anal Prev 42(5):1498–1506. https://doi.org/10.1016/j.aap.2010.01.003
Cocca P, Marciano F, Alberti M (2016) Video surveillance systems to enhance occupational safety: a case study. Saf Sci 84:140–148. https://doi.org/10.1016/j.ssci.2015.12.005
Conchie SM, Moon S, Duncan M (2013) Supervisors’ engagement in safety leadership: factors that help and hinder. Saf Sci 51(1):109–117. https://doi.org/10.1016/j.ssci.2012.05.020
Fang DP, Wu HJ (2013) Development of a safety culture interaction (SCI) model for construction projects. Saf Sci 57:138–149. https://doi.org/10.1016/j.ssci.2013.02.003
Fang DP, Wu CL, Wu HJ (2015) Impact of the supervisor on worker safety behavior in construction projects. J Manag Eng 31(6):04015001. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000355
Fang DP, Zhao C, Zhang MC (2016) A cognitive model of construction workers’ unsafe behaviors. J Constr Eng Manag 142(9):04016039. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001118
Goh YM, Ali M (2016) A hybrid simulation approach for integrating safety behavior into construction planning: an earthmoving case study. Accid Anal Prev 93:310–318. https://doi.org/10.1016/j.aap.2015.09.015
Grill M, Nielsen K (2019) Promoting and impeding safety—a qualitative study into direct and indirect safety leadership practices of constructions site managers. Saf Sci 144:148–159. https://doi.org/10.1016/j.ssci.2019.01.008
Guo SY, Xiong CH, Gong PS (2018) A real-time control approach based on intelligent video surveillance for violations by construction workers. J Constr Eng Manag 24(1):67–78. https://doi.org/10.3846/jcem.2018.301
Health and Safety Executive (2020) Construction Statistics in Great Britain, 2020. Health and Safety Executive. https://www.hse.gov.uk/statistics/industry/construction.pdf. Accessed 4 Nov 2020
Hijazi AM, AbouRizk SM, Halpin DW (1992) Modeling and simulating learning development in construction. J Constr Eng Manag 118(4):685–700. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:4(685)
Hoffmeister K, Gibbons AM, Johnson SK, Cigularov KP, Chen PY, Rosecrance JC (2014) The differential effects of transformational leadership facets on employee safety. Saf Sci 62:68–78. https://doi.org/10.1016/j.ssci.2013.07.004
Jarkas AM (2010) Critical investigation into the applicability of the learning curve theory to rebar fixing labor productivity. J Constr Eng Manag 136(12):1279–1288. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000236
Jasmine VB, Syaifullah DB, Moch BN (2019) Evaluation of influence organizational factors and psychological factors for safe work behavior in construction projects in DKI Jakarta. In: Proceedings of the 5th international conference on industrial and business engineering (ICIBE), September 27–29, Hong Kong, China
Jiang ZM (2014) Modeling and simulation of safety-related behaviors in building construction. Dissertation, Tsinghua University
Jiang ZM, Fang DP (2014) Confidence Building of a system dynamics model on the causation of construction workers’ unsafe behaviors. In: Proceedings of the construction research congress 2014, May 19–21, Atlanta, Georgia
Jiang H, Wang JP, Yu H, Yin HG (2018) Structural equation model analysis of factors in the spread of unsafe behavior among construction workers. Information 9(2):1–11. https://doi.org/10.3390/info9020039
Kines P, Andersen LPS, Spangenberg S, Mikkelsen KL, Dyreborg J (2010) Improving construction site safety through leader-based verbal safety communication. J Saf Res 41(5):399–406. https://doi.org/10.1016/j.jsr.2010.06.005
Li H, Lu M, Hsu SC, Gray M, Huang T (2015) Proactive behavior-based safety management for construction safety improvement. Saf Sci 75(June):107–117. https://doi.org/10.1016/j.ssci.2015.01.013
Li H, Li XY, Luo XC, Siebert J (2017) Investigation of the causality patterns of non-helmet use behavior of construction workers. Autom Constr 80:95–103. https://doi.org/10.1016/j.autcon.2017.02.006
Liang X, Yu T, Hong JK, Shen QP (2019) Making incentive policies more effective: an agent-based model for energy-efficiency retrofit in China. Energy Policy 126:177–189. https://doi.org/10.1016/j.enpol.2018.11.029
Lu MJ, Cheung CM, Li H, Hsu SC (2016) Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling. Accid Anal Prev 94:8–17. https://doi.org/10.1016/j.aap.2016.05.014
Macal CM, North MJ (2010) Tutorial on agent-based modelling and simulation. J Simul 4(3):151–162. https://doi.org/10.1057/jos.2010.3
Man SS, Chan A, Alabdulkarim S, Zhang T (2021) The effect of personal and organizational factors on the risk-taking behavior of Hong Kong construction workers. Saf Sci 136(3):105155. https://doi.org/10.1016/j.ssci.2020.105155
Martin-Vega FJ, Soret B, Aguayo-Torres MC, Kovacs IZ, Gomez G (2017) Geo-location based access for vehicular communications: analysis and optimization via stochastic geometry. IEEE Trans Veh Technol 67(4):3069–3084. https://doi.org/10.1109/TVT.2017.2775249
Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2020) Notice on Production Safety Accidents of Housing and Municipal Engineering in 2019. Ministry of Housing and Urban-Rural Development of the People’s Republic of China. https://www.mohurd.gov.cn/gongkai/fdzdgknr/tzgg/202006/20200624_246031. htmlhttp://www.mohurd.gov.cn/wjfb/202006/t20200624_246031.html. Accessed 19 June 2020
Mohan S, Duarte D (2006) Cognitive modeling of underground miners response to accidents. In: Proceedings of the 52nd annual reliability and maintainability symposium, January 23–26, Newport Beach, CA, USA
Molinero C, Núñez M (2011) Planning of work schedules through the use of a hierarchical multi-agent system. Autom Constr 20(8):1227–1241. https://doi.org/10.1016/j.autcon.2011.05.006
Nakayasu H, Nakagawa M, Miyoshi T, Aoki H (2010) Human factor on driver and human cognitive reliability by driving simulator. Proceedings of the 9th Pan-Pacific Conference on Ergonomics (PPCOE 2010), November 07–10, Kaohsiung, Taiwan, China
Neal A, Griffin MA, Hart PM (2000) The impact of organizational climate on safety climate and individual behavior. Saf Sci 34(1):99–109. https://doi.org/10.1016/S0925-7535(00)00008-4
Office for National Statistics (2021) A01: summary of labour market statistics. Office for National Statistics. https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/datasets/summaryoflabourmarketstatistics. Accessed 16 Nov 2021
Ormerod P, Rosewell B (2009) Validation and verification of agent-based models in the social sciences. In: Proceedings of the 2nd international workshop on epistemological perspectives on simulation, October 05–06, Univ Brescia, Brescia, Italy
Pandit B, Albert A, Patil Y, Al-Bayati AJ (2019) Fostering safety communication among construction workers: role of safety climate and crew-level cohesion. Int J Environ Res Public Health 16(1):1–16. https://doi.org/10.3390/ijerph16010071
Probst TM (2004) Safety and insecurity: exploring the moderating effect of organizational safety climate. J Occup Health Psychol 9(1):3–10. https://doi.org/10.1037/1076-8998.9.1.3
Rasmussen J (1987) Information processing and human-machine interaction: an approach to cognitive engineering. Elsevier, Amsterdam
Roxburgh N, Evans A, Gc RK, Malleson N, Heppenstall A, Stringer L (2021) An empirically informed agent-based model of a Nepalese smallholder village. MethodsX 8:101276. https://doi.org/10.1016/j.mex.2021.101276
Sattenspiel L, Dimka J, Orbann C (2019) Using cultural, historical, and epidemiological data to inform, calibrate, and verify model structures in agent-based simulations. Math Biosci Eng 16(4):3071–3093. https://doi.org/10.3934/mbe.2019152
Shin DP, Gwak HS, Lee DE (2015) Modeling the predictors of safety behavior in construction workers. Int J Occup Saf Ergon 21(3):298–311. https://doi.org/10.1080/10803548.2015.1085164
Suleiman R, Troitzsch KG, Gilbert N (2012) Tools and techniques for social science simulation. Springer, Berlin
Sunindijo RY, Zou PXW, Dainty ARJ (2017) Managerial skills for managing construction safety. Civ Eng Dimens 19(2):63–72. https://doi.org/10.9744/ced.19.2.63-72
U.S. Bureau of Labor Statistics (2020) Census of Fatal Occupational Injuries in Summary, 2019. U.S. Bureau of Labor Statistics. https://www.bls.gov/news.release/cfoi.nr0.htm. Accessed 16 Dec 2020
Wachter JK, Yorio PL (2014) A system of safety management practices and worker engagement for reducing and preventing accidents: an empirical and theoretical investigation. Accid Anal Prev 68(7):117–130. https://doi.org/10.1016/j.aap.2013.07.029
Wehbe F, Hattab MA, Hamzeh F (2016) Exploring associations between resilience and construction safety performance in safety networks. Saf Sci 82:338–351. https://doi.org/10.1016/j.ssci.2015.10.006
Wickens CD (1984) Engineering psychology and human performance. HarperCollins Publishers, Columbus
Wright TP (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3:122–128. https://doi.org/10.2514/8.155
Xu SY, Wang J, Shou WC, Ngo T, Sadick AM, Wang XY (2020) Computer vision techniques in construction: a critical review. Arch Comput Methods Eng 28:3383–3397. https://doi.org/10.1007/s11831-020-09504-3
Ye G, Yue HZ, Yang JJ, Li HY, Xiang QT, Fu Y, Cui C (2020) Understanding the sociocognitive pocess of construction workers’ unsafe behaviors: an agent-based modeling approach. Int J Environ Res Public Health 17(5):1588–1620. https://doi.org/10.3390/ijerph17051588
Yule S, Flin R, Murdy A (2007) The role of management and safety climate in preventing risk-taking at work. Int J Risk Assess Manag 7(2):137–151
Zhang MC, Fang DP (2013) A cognitive analysis of why Chinese scaffolders do not use safety harnesses in construction. Constr Manag Econ 31(3):207–222. https://doi.org/10.1080/01446193.2013.764000
Zhang PY, Li N, Jiang ZM, Fang DP, Anumba CJ (2019) An agent-based modeling approach for understanding the effect of worker-management interactions on construction workers’ safety-related behaviors. Autom Constr 97:29–43. https://doi.org/10.1016/j.autcon.2018.10.015
Acknowledgements
This work was supported by the National Social Science Foundation of China (Grant No. 19BGL238). Gratitude is expressed to those who offered suggestions to this paper. Sincere gratitude and respect were expressed to all reviewers.
Funding
The authors have no relevant financial or non-financial interests to disclose.
Author information
Authors and Affiliations
Contributions
Lu Ying provided funding acquisition, methodology, and writing—original draft; Liu Suhui was involved in software and data curation; Li Chaozhi investigated the study.
Corresponding author
Rights and permissions
About this article
Cite this article
Lu, Y., Liu, S. & Li, C. Understanding the Effect of Management Factors on Construction Workers’ Unsafe Behaviors Through Agent-Based Modeling. Iran J Sci Technol Trans Civ Eng 47, 1251–1263 (2023). https://doi.org/10.1007/s40996-022-00898-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40996-022-00898-7