Consequence of Failure: Neurofuzzy-Based Prediction Model for Gas Pipelines

Abstract

Overall performance of energy infrastructure in the United States has been assessed as D+. More than 65% of America’s energy is transported through the oil and gas pipelines, which have experienced more than 10,000 failures during the last three decades. There is a critical need for a failure prediction tool that can forecast the consequences of the hazardous failures. Failure of gas pipelines has become the subject of interest for some studies in the past. Previous studies mainly focused on physical models that need inspection data or developed subjective models. This paper aims at developing a model to forecast the consequences of the potential failures of such pipes using the historical data of the U.S. gas pipes network. The model applies a neurofuzzy technique in order to recognize the existing pattern among the input and output variables. It estimates the financial consequences of various failure scenarios for specific pipes in terms of size and specified minimum yield strength. For this purpose, a bowtie model is developed, and all possible scenarios of failure are identified. Various combinations of the identified factors and different number and types of membership functions, are applied in order to optimize the model’s efficiency. The developed model is validated with an approximate accuracy of 80%. This study assists practitioners and academics who are working on the risk assessment of gas pipelines to plan for their lifecycle inspection.