Workers’ unsafe behaviors primarily cause accidents in workplaces. Despite extensive research on the antecedents of unsafe behaviors, understanding workers’ dynamic decision-making under the influence of safety cognition, group interactions, management, site conditions, and behavioral outcomes as a holistic system remains limited.
Objective
This study aims to explore workers’ safety performance across different site risk environments and various intervention measures, including the effects of intervention frequency, punishment strictness, and interaction intensity on dynamic safety performance in low, medium, and high-risk environments.
Methods
An agent-based simulation model considering multiple factors of individual safety cognition and behavioral decision-making was conducted. It examined the complex roles of workers’ cognitive decision-making processes within workgroup interactions, management strategies, and environmental contexts.
Results
The results reveal that: (a) management interventions effectively reduce unsafe behaviors across all risk environments; (b) punishment strictness significantly inhibits unsafe behaviors, with moderate punishment being most effective in low-risk environments; (c) the impact of worker interactions varies significantly by risk level—limiting interactions in low-risk environments, promoting interactions in medium-risk environments, and additional measures are needed in high-risk environments to reduce unsafe behaviors.
Conclusion
This research lays a new foundation for the causes of workers’ unsafe behaviors when corresponding with effective management strategies. It also provides valuable insights for project managers in cognitive management and policy analysis.
Trillo CabelloAMartínez-RojasMCarrillo-CastrilloJA, et al.Occupational accident analysis according to professionals of different construction phases using association rules. Saf Sci2021; 144: 105457.
2.
XiangQYeGLiuY, et al.Cognitive mechanism of construction workers’ unsafe behavior: a systematic review. Saf Sci2023; 159: 106037.
3.
LovePEDIkaLLuoH, et al.Rework, failures, and unsafe behavior: moving toward an error management mindset in construction. IEEE Trans Eng Manage2022; 69: 1489–1501.
4.
LeeBGChoiBJebelliH, et al.Assessment of construction workers’ perceived risk using physiological data from wearable sensors: a machine learning approach. J Build Eng2021; 42: 102824.
5.
LiuYYeGXiangQ, et al.Antecedents of construction workers’ safety cognition: a systematic review. Saf Sci2023; 157: 105923.
6.
FangDWuH. Development of a Safety Culture Interaction (SCI) model for construction projects. Saf Sci2013; 57: 138–149.
7.
GuimaraesACavalcantiMVasconcelosBM. Gamification using technologies for occupational safety training in the civil construction sector. Work2024; 77: 477–485.
8.
FangDWuCWuH. Impact of the supervisor on worker safety behavior in construction projects. J Manage Eng2015; 31: 04015001.
9.
ZhangPLiNFangD, et al.Supervisor-Focused behavior-based safety method for the construction industry: case study in Hong Kong. J Constr Eng Manage2017; 143: 05017009.
10.
FangDZhaoCZhangM. A cognitive model of construction Workers’ unsafe behaviors. J Constr Eng Manage2016; 142: 04016039.
11.
KaraibrahimogluAInceFHassanzadehG, et al.Ethical considerations in telehealth and artificial intelligence for work related musculoskeletal disorders: a scoping review. Work2024; 79: 1577–1588.
12.
DengSCaiYXieL, et al.Group management model for construction workers’ unsafe behavior based on cognitive process model. Eng Constr Archit Manag2023; 30: 2928–2946.
13.
FangQChenXCastro-LacoutureD, et al.Intervention and management of construction workers’ unsafe behavior: a simulation digital twin model. Adv Eng Inform2023; 58: 102182.
14.
NingXYangYLiuCY, et al.Construction workers’ unsafe behavior contagion under government-contractor dual influence. J Constr Eng Manage2024; 150. doi:10.1061/JCEMD4.COENG-14282
15.
GaoYGonzálezVAYiuTW. Exploring the relationship between construction Workers’ personality traits and safety behavior. J Constr Eng Manage2020; 146: 04019111.
16.
ZhangZXiangTGuoH, et al.Impact of physical and mental fatigue on construction workers’ unsafe behavior based on physiological measurement. J Saf Res2023; 85: 457–468.
17.
AnYWangHYangX, et al.Using the TPB and 24Model to understand workers’ unintentional and intentional unsafe behaviour: a case study. Saf Sci2023; 163: 106099.
18.
HeCMcCabeBJiaG, et al.Effects of safety climate and safety behavior on safety outcomes between supervisors and construction workers. J Constr Eng Manage2020; 146: 04019092.
19.
XiaNXieQHuX, et al.A dual perspective on risk perception and its effect on safety behavior: a moderated mediation model of safety motivation, and supervisor’s and coworkers’ safety climate. Accid Anal Prev2020; 134: 105350.
20.
ZhangZLiHGuoH, et al.Causal inference of construction safety management measures towards workers’ safety behaviors: a multidimensional perspective. Saf Sci2024; 172: 106432.
21.
LiangHZhangS. Impact of supervisors’ safety violations on an individual worker within a construction crew. Saf Sci2019; 120: 679–691.
22.
NewazMTDavisPJefferiesM, et al.Examining the psychological contract as mediator between the safety behavior of supervisors and workers on construction sites. J Constr Eng Manage2020; 146: 04019094.
23.
TangSGolparvar-FardM. Machine learning-based risk analysis for construction worker safety from ubiquitous site photos and videos. J Comput Civil Eng2021; 35: 04021020.
24.
HuangYPWangXQDingRX, et al. (eds) Risk perception, risk propensity, and unsafe behavior: An empirical study of workers in Chinese construction industry. In: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 4–7 December 2016. 2016.
25.
HanYDiaoYYinZ, et al.Immersive technology-driven investigations on influence factors of cognitive load incurred in construction site hazard recognition, analysis and decision making. Adv Eng Inform2021; 48: 101298.
26.
WangYChongH-YLiaoP-C, et al.Interactive mechanism of working environments and construction behaviors with cognitive work analysis: an elevator installation case study. Int J Occup Saf Ergon2019; 25: 362–376.
RasmussenJ. Risk management in a dynamic society: a modelling problem. Saf Sci1997; 27: 183–213.
29.
WickensCDHollandsJGBanburyS, et al. (eds)Engineering Psychology and Human Performance. Routledge, 2021.
30.
ChangYHJMoslehA. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: part 1: overview of the IDAC model. Reliab Eng Syst Saf2007; 92: 997–1013.
31.
ChoiBLeeS. An empirically based agent-based model of the sociocognitive process of construction Workers’ safety behavior. J Constr Eng Manage2018; 144: 04017102.
32.
DengSZhuHPengR, et al.Development and validation of a cognitive model-based novel questionnaire for measuring potential unsafe behaviors of construction workers. Int J Occup Saf Ergon2022; 28: 2566–2573.
33.
HuZChanWTHuH, et al.Cognitive factors underlying unsafe behaviors of construction workers as a tool in safety management. A Review. J Constr Eng Manage2023; 149: 03123001.
34.
KedirNSFayekAR. Integrated fuzzy system dynamics-fuzzy agent-based modeling of construction crew motivation and productivity. Adv Eng Inform2025; 66: 103367.
35.
Yu S-m, FanYZhuL, et al.Modeling the emission trading scheme from an agent-based perspective: system dynamics emerging from firms’ coordination among abatement options. Eur J Oper Res2020; 286: 1113–1128.
36.
LuMCheungCMLiH, et al.Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling. Accid Anal Prev2016; 94: 8–17.
37.
MahjoubpourBNasirzadehFMohammad Hosein Zadeh GolabchiM, et al.Modeling of workers’ learning behavior in construction projects using agent-based approach. Eng Constr Archit Manag2018; 25: 559–573.
38.
WuCChenCJiangR, et al.Understanding laborers’ behavioral diversities in multinational construction projects using integrated simulation approach. Eng Constr Archit Manag2019; 26: 2120–2146.
39.
AwwadRShdidCATayehR. Agent-Based model for simulating construction safety climate in a market environment. J Comput Civil Eng2017; 31: 05016003.
40.
RaoufiMFayekAR. Fuzzy agent-based modeling of construction crew motivation and performance. J Comput Civil Eng2018; 32: 04018035.
41.
ZhangAZhenQZhengC, et al.Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: an agent-based approach. J Build Eng2023; 74: 106807.
42.
HeCJiaGMcCabeB, et al.Psychological decision-making process of construction worker safety behavior: an agent-based simulation approach. Int J Occup Saf Ergon2023; 29: 141–153.
43.
ZhangSHuaXShiX. Measurement for risk perception ability. J Constr Eng Manage2024; 150: 04024036.
44.
LuYLiuSLiC. Understanding the effect of management factors on construction Workers’ unsafe behaviors through agent-based modeling. Iran J Sci Technol-Trans Civ Eng2023; 47: 1251–1263.
WildeGJS. The theory of risk homeostasis: implications for safety and health. Risk Anal1982; 2: 209–225.
47.
McSweenTMoranDJ. Assessing and preventing serious incidents with behavioral science: enhancing Heinrich's triangle for the 21st Century. J Organ Behav Manage2017; 37: 283–300.
48.
HeinrichHW. Industrial accident prevention: a scientific approach, 1951.
49.
MurreJMJ. How averaging individual curves transforms their shape: mathematical analyses with application to learning and forgetting curves. J Math Psychol2023; 117: 102816.
50.
FerreiraJAValmorbidaELSatoBG, et al.Forgetting curve models: a systematic review aimed at consolidating the main models and outlining possibilities for future research in production. Expert Syst2024; 41: e13405.
51.
MuMYuanM. Research on a personalized learning path recommendation system based on cognitive graph with a cognitive graph. Interact Learn Environ2024; 32: 4237–4255.
52.
HijaziAMAbouRizkSMHalpinDW. Modeling and simulating learning development in construction. J Constr Eng Manage1992; 118: 685–700.
53.
DengSZhuHCaiY, et al.Group cognitive characteristics of construction Workers’ unsafe behaviors from personalized management. Saf Sci2024; 175: 106492.
54.
ZhangPLiNJiangZ, et al.An agent-based modeling approach for understanding the effect of worker-management interactions on construction workers’ safety-related behaviors. Autom Constr2019; 97: 29–43.
55.
HeinrichHW. Industrial accident prevention: a safety management approach. New York, NY, USA: McGraw-Hill, 1980.
56.
WangJZouPXWLiPP. Critical factors and paths influencing construction workers’ safety risk tolerances. Accid Anal Prev2016; 93: 267–279.
57.
LiWZZhouLJHaoJ, et al.Dynamic simulation and control strategy exploration of the unsafe behavior of coal mine employees. Resour Policy2023; 86: 104067.
58.
KimNParkSUhmM, et al.Enhancing construction Managers’ risk perception and lowering risk tolerance toward unsafe behaviors through experiential safety training. J Manage Eng2025; 41: 04024067.
59.
DengSNiPZhuH, et al.Artificial cognition to predict and explain the potential unsafe behaviors of construction workers. J Constr Eng Manage2024; 150: 04024074.
60.
LuoYYangQSeoJ, et al.Theoretical framework for utilizing eye-tracking data to understand the cognitive mechanism of situational awareness in construction hazard recognition. J Manage Eng2024; 40: 04024027.
61.
CremeneLCremeneM. The social honesty game - A computational analysis of the impact of conformity and identity on honest behavior contagion in complex social systems. Chaos Solitons Fractals2021; 144: 110710.
62.
GuoSTangBLiangK, et al.Comparative analysis of the patterns of unsafe behaviors in accidents between building construction and urban railway construction. J Constr Eng Manage2021; 147: 04021027.
