Procedural Framework for Modeling the Likelihood of Failure of Underground Pipeline Assets

Abstract

AbstractReliable prediction of asset condition and its likelihood of failure is one of the core requirements for a utility to establish effective asset management strategies for optimized maintenance, rehabilitation, and replacement plans. Although there have been many research efforts in academia to predict the failure of pipe assets, many utilities across the United States still find it challenging to effectively predict the likelihood of failure (LOF) of their pipeline assets. Most of them still use subjective scales and rely on engineers’ anecdotal experience and judgments. This study developed a holistic procedural framework that utilities can follow to develop a data driven LOF prediction model of their pipeline assets. The unique contribution of this paper is that the framework addresses issues that a utility will encounter from data collection and data organization to LOF prediction model development, and discusses possible solutions as well. Historical performance records of sewer pipes from a ma...