Insights from Applying Association Rule Mining to Pipeline Incident Report Data

Pipelines are the critical component of energy infrastructure for safely transporting large volumes of oil and hazardous materials over long distances. However, many incidents occurred over the years, leading to shutdowns, disruptions, and economic losses. The Pipeline and Hazardous Materials Safety Administration (PHMSA) maintains those incidents' reports, constituting a valuable resource for better understanding the underlying associations between those incidents' causes and shutdown durations. Most previous studies investigating incident databases are focused on bivariate statistical analyses between cause and effect variables, providing little to no insight into their associations. Also, little research has examined the level of disruptions caused by an incident. This study proposes a novel application of association rule mining to the PHMSA database to extract associations and insights between causal factors, background factors (e.g., materials), and their effects, particularly shutdown duration in this study. The results are expected to help improve pipeline infrastructure's planning, design, and operations.