Assessing the off-site manufacturing workers' influence on safety performance: A Bayesian network approach

Vithanage, S C; Sing, M C P; Davis, P and Newaz, M T (2022) Assessing the off-site manufacturing workers' influence on safety performance: A Bayesian network approach. Journal of Construction Engineering and Management, 148(1): 04021185, ISSN 0733-9364

Abstract

Off-site manufacturing (OSM) offers a wide range of benefits to the construction industry by saving time, reducing waste, being an environmentally friendly solution, and providing a much safer onsite environment. However, safety management of OSM has become a concern due to the worker-related safety issues, which increase the safety incidents in off-site factories. However, research on the safety of OSM activities within a factory environment are still very limited. Therefore, this study aims to examine the interrelationships among worker-related safety factors and their influence on OSM safety performance. A probabilistic model based on Bayesian networks (BNs) to assess the influence of worker-related safety climate factors on OSM safety performance is utilized in this research study. A comprehensive review and evaluation of causal safety factors with the support of a questionnaire survey with OSM industry practitioners are the basis for this BN model. The proposed BN model is then verified by conducting a sensitivity analysis such as tornado diagrams and derivatives of sensitivity. The established model presents probabilities associated with different states of safety factors and identifies the interrelationships among worker-related safety climate factors and OSM safety performance. The research findings show that improvements in management safety response, coworkers' safety values and practices, and quality of training exert a greatest effect on the safety performance. Analysis further indicates that a balance between safety and production significantly affects workers' safety knowledge. This study contributes to the OSM safety domain by offering an effective tool to predict safety performance.

Item Type: Article
Uncontrolled Keywords: Bayesian networks; off-site manufacturing; safety climate; safety performance
Index terms: survey, safety issue, sensitivity analysis, safety climate, off-site manufacturing, safety performance, safety management, practitioner, questionnaire, safety factor, bayesian network, onsite, environmentally friendly, construction industry
Subjects: practitioner, data collection methods, health safety and environment, sustainable design, financial risk, manufacturing engineering, industry analysis, environmental hazards, occupational health and safety management, probabilistic model, building construction
Topics: Research Practice, Engineering Principles, Cost Management, Health and Safety, Construction Technology, Sustainability, Roles and Professions, Digital Applications
Descriptive scope: 5 PCTEA

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