| dc.description.abstract | The current oil pipeline system is at risk for leaks and ruptures due to aging infrastructure, corrosion, and extreme weather. Addressing these vulnerabilities demands a robust disaster preparedness and response strategy that can be adapted to various incident causes. This study evaluates a data-driven framework with artificial intelligence (AI)–based algorithms for classifying failure causes in oil pipelines. For this purpose, a data set summarizing oil pipeline incidents from 2010 to 2022 obtained from the Pipeline and Hazardous Materials Safety Administration, which comprises various incident attributes, including the severity of incidents and estimated financial impacts, was processed for data balancing with a variation of synthetic minority oversampling technique. Further, this study employs extreme gradient boosting gradient, random forest, K-nearest neighbors, and support vector machine algorithms for multiclassification tasks in predicting the failure cause. The results indicate that majority-class incident causes can be effectively predicted across algorithms, achieving over 90% accuracy for corrosion failures and over 80% for equipment failures. The proposed methods can be integrated into the mitigation stage of a disaster management framework to aid with decision-making in maintenance activities and improve emergency response based on incident urgency. The practical application of this study lies in enhancing disaster preparedness and response for oil pipelines. By utilizing AI-based algorithms to classify and predict failure causes (such as corrosion, equipment malfunctions, or external factors), the proposed data-driven framework offers a proactive approach to pipeline maintenance and emergency response. This predictive capability improves the decision-making process by enabling operators to detect potential failures before they escalate into serious incidents, allowing for timely interventions. With high accuracy in predicting issues like corrosion and equipment degradation, the model can help prioritize maintenance tasks based on the severity and urgency of predicted failures. This targeted approach reduces the likelihood of pipeline leaks and ruptures and helps minimize the associated financial losses, environmental damage, and safety risks. This framework enhances decision-making, streamlines resource allocation, and contributes to more resilient and sustainable pipeline infrastructure. | |
