Fall risk assessment of bridge construction using Bayesian network transferring from fault tree analysis
Falling is the most common one during bridge construction. Current safety management on site mainly relies on checklist assessment. Yet the assessment result is often influenced by the ability and experience of the evaluator, thus is not impossible to achieve consistent and systematic assessment objective. Moreover, most critical factors that can prevent occurrence of accidents cannot be found from existing safety management and assessment method. This paper built a Bayesian Network (BN) model by converting Fault Tree to assess the fall risk of bridge construction projects. We analyse falling factors and their relationships in Bayesian Network, and collect prior probability event and calculate the probability for the entire model. Using the model to analyse and validate with the current bridge projects under construction, the results from Bayesian Network is consistent with that from conventional labour safety performance assessment. Therefore, the ability to manage site safety of the model is proven to be useful.
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