Bayesian networks offer an intuitive method of modelling causal relationships between the triggering events that lead to equipment impact on a pipeline. This method offers an advantage over the more well-known fault-tree methods due to its ability to use Bayesian inference for updating the prior probabilities of triggering events that lead to equipment impact such as, failure of permanent markers, use of one-call system, or failure of right-of-way patrol. In this paper, a modelling approach for a Bayesian network for equipment impact assessment, based on the available fault-tree method, is demonstrated. The advantages of the Bayesian network, such as updating the occurrence rates of basic triggering events and tracking information flow based on partial and incomplete information are illustrated by using the event data available from the damage incident reporting tool (DIRT) of Common Ground Alliance (CGA).

This content is only available via PDF.
You do not currently have access to this content.