Predicting Safety Risk of Working at Heights Using Bayesian Networks

Abstract

AbstractAlthough the construction industry has shown significant improvements in safety performance over the past 30 years, falls are still a leading cause of fatalities and serious injuries. Previous studies have focused on identifying factors affecting the risk of falls, but remained silent on investigating the evidential relationships among these factors to better prevent fall accidents. This research proposes a Bayesian network (BN) based approach to diagnose the accident risk of working at heights. The proposed approach consists of a conceptual and generic model with a protocol for assessing the risk of falls and a computational module. The generic BN model was developed on the basis of an extensive review and evaluation of causal factors leading to falls. The computational module was developed on the basis of Bayes’ rule for inference to customize model input and job site characteristics. The results of the proposed approach provide probabilities associated with different states of safety risk. Addi...