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Abstract
A fall (also referred to as a tumble) is the most common type of accident at steel construction (SC) sites. To reduce the risk of falls, current site safety management relies mainly on checklist evaluations. However, current on-site inspection is conducted under passive supervision, which fails to provide early warning to occupational accidents. To overcome the limitations of the traditional approach, this paper presents the development of a fall risk assessment model for SC projects by establishing a Bayesian network (BN) based on fault tree (FT) transformation. The model can enhance site safety management through an improved understanding of the probability of fall risks obtained from the analysis of the causes of falls and their relationships in the BN. In practice, based on the analysis of fall risks and safety factors, proper preventive safety management strategies can be established to reduce the occurrences of fall accidents at SC sites.
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Notes on contributors
Sou-Sen Leu
Sou-Sen LEU. Professor at the Department of Construction Engineering, National Taiwan University of Science and Technology. Research interests: construction risk management, data mining, construction performance management, green building assessment, computational optimization, and information technology.
Ching-Miao Chang
Ching-Miao CHANG. PhD student at the Department of Construction Engineering, National Taiwan University of Science and Technology. General Manager of Ruentex Construction Company. CEO of RSEA Engineering Corporation. Research interests: construction engineering technology and management, health and safety management.