A Study on Human Behavior and its impact on construction
DOI:
https://doi.org/10.29070/94xzjh83Keywords:
Human behaviour, Construction safety, Behavioral safety, Artificial intelligenceAbstract
It is one of the largest industrial sectors in the world economy and one of the occupational accident deathliest industries in the world in terms of the number of occupational accidents caused by unsafe human behaviours. This study is aimed at understanding the influence of human behavior on safety in construction sites and identifying the various factors influencing accidents and their severity on the construction sites including behavioral, psychological, physical and environmental factors. Descriptive and an analytical research design was used, and structured questionnaires, interviews and site observations were employed to collect the data, which comprised 400 respondents comprising of construction workers, site supervisors, safety officers and project managers. The data collected were analyzed using percentage analysis, descriptive statistics, correlation analysis and regression analysis using SPSS and microsoft office excel. The results demonstrate that unsafe behaviors are the most important factor affecting construction accidents; lack of safety awareness, fatigue, and job stress are the next most important factors. The study results highlight a high potential for reducing accidents on construction projects and enhancing overall project performance through increased safety awareness among workers, improvements in behavioral safety management, increased training frequency, reduction of occupational stress and fatigue and the use of contemporary technologies such as artificial intelligence and behaviour monitoring systems.
Downloads
References
1. Badke-Schaub, P., & Schaub, H. (2022). Human behavior, roles, and processes. In Handbook of Engineering Systems Design (pp. 1-36). Cham: Springer International Publishing.
2. Jain, S., Singhal, S., Jain, N. K., & Bhaskar, K. (2020). Construction and demolition waste recycling: Investigating the role of theory of planned behavior, institutional pressures and environmental consciousness. Journal of Cleaner Production, 263, 121405.
3. Jiang, L., Zou, B., Liu, S., Yang, W., Wang, M., & Huang, E. (2023). Recognition of abnormal human behavior in dual-channel convolutional 3D construction site based on deep learning. Neural Computing and Applications, 35(12), 8733-8745.
4. Li, S., & Zhu, A. (2024, July). Recent advancements with human behavior recognition and ai in construction automation: A literature review. In 2024 International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 759-764). IEEE.
5. Lu, C., Yu, D., Luo, Q., & Xu, C. (2023). A study of the effects of job stress on the psychosocial safety behavior of construction workers: the mediating role of psychological resilience. Buildings, 13(8), 1930.
6. Meng, Q., Liu, W., Li, Z., & Hu, X. (2021). Influencing factors, mechanism and prevention of construction workers’ unsafe behaviors: A systematic literature review. International journal of environmental research and public health, 18(5), 2644.
7. Obolewicz, J., Baryłka, A., & Szota, M. (2023). The impact of human behaviour on the (un) safety of the construction site. Journal of Achievements in Materials and Manufacturing Engineering, 119(1).
8. Ponizovskiy, V., Grigoryan, L., Kühnen, U., & Boehnke, K. (2019). Social construction of the value–behavior relation. Frontiers in psychology, 10, 934.
9. Ready, E., & Price, M. H. (2021). Human behavioral ecology and niche construction. Evolutionary Anthropology: Issues, News, and Reviews, 30(1), 71-83.
10. Schia, M. H., Lædre, O., & Fyhn, H. (2019). The introduction of AI in the construction industry and its impact on human behavior. In Proc. 27th Annual Conference of the International Group for Lean Construction (IGLC).
11. Shi, Y., Du, J., Ahn, C. R., & Ragan, E. (2019). Impact assessment of reinforced learning methods on construction workers' fall risk behavior using virtual reality. Automation in Construction, 104, 197-214.
12. Ye, G., Yue, H., Yang, J., Li, H., Xiang, Q., Fu, Y., & Cui, C. (2020). Understanding the sociocognitive process of construction workers’ unsafe behaviors: An agent-based modeling approach. International journal of environmental research and public health, 17(5), 1588.
13. Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models. Buildings, 14(1). https://doi.org/10.3390/buildings14010220
14. Li, J., Miao, Q., Zou, Z., Gao, H., Zhang, L., Li, Z., & Wang, N. (2024). A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors. IEEE Access, 12(December 2023), 7134–7155. https://doi.org/10.1109/ACCESS.2024.3350773
15. Michalik, J. (2023). overview of methods for investigating accidents at work based on an accident in a manufacturing company. 8(1), 146–156. https://doi.org/10.2478/czoto-2023-0014
16. Primadi Candra Susanto, Siera Syailendra, & Ryan Firdiansyah Suryawan. (2023). Determination of Motivation and Performance: Analysis of Job Satisfaction, Employee Engagement and Leadership. International Journal of Business and Applied Economics, 2(2), 59–68. https://doi.org/10.55927/ijbae.v2i2.2135
17. Yang, S., Liu, L., Wang, T., Guo, Y., Qian, Y., & Chen, H. (2025). The Impact of Accident Experience on Unsafe Behaviors of Construction Workers Within Social Cognitive Theory. Buildings, 15(1), 1–19. https://doi.org/10.3390/buildings15010059
