Data-driven Bayesian networks for risk scenario mapping of Falls from height accidents.

ABSTRACT: Falls from height (FFH) represent the most frequent type of accident in the building industry, leading to substantial economic losses and posing serious threats to worker safety. While risk analysis plays a vital role in accident prevention, a more comprehensive understanding of risk can significantly contribute to reducing the occurrence of accidents. To better capture the complexity and uncertainty inherent in risk factors, this study adopted the concept of risk scenarios to investigate the underlying mechanisms and driving factors associated with FFH accidents. A total of 368 FFH accident reports from 2014 to 2024 were collected, and a Bayesian network model was developed based on the validated data extracted from these reports. Through this model, various risk scenarios of FFH accident

SUBMITTER: Li J 

PROVIDER: S-EPMC12520409 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature