A Mini Review on Predicting Buried Pipelines Failures Using Machine Learning Methods (ANN and Hybrid)

Abstract:

Maintaining safe pipeline conditions is crucial to ensure sustainable and reliable transportation for energy and water. Pipelines are generally laid underground due to larger transport capacity, rapid construction speed, space restriction, and safety precautions. Nevertheless, they are prone to failures due to mechanical problems, extreme operation, and aggressive surrounding environmental conditions. The usage of machine learning methods to predict buried pipeline failures has risen recently due to its effectiveness in addressing the aforementioned problems. This paper reviews making predictions on different buried pipeline failures by adopting machine learning approaches, particularly artificial neural networks (ANN) and hybrid methods. It highlights the detail of the machine learning algorithms as well as the parameters that were used in the predictive models with concise elaboration. Findings show that the ANN method gives accurate failure prediction, while the hybrid method enhances the prediction accuracy. Nevertheless, there is no single absolute algorithm that can work best to solve all pipeline failures. Finding the most suitable machine learning algorithm for a specific pipeline failure will be a challenge to overcome. This review is expected to give more comprehension to industry players related to machine learning methods as a potential tool to solve various buried pipeline problems. Further, this review may prompt other interested researchers to further discover machine learning potentials and ways to increase its effectiveness.

• Periodical: Applied Mechanics and Materials (Volume 929)
• Pages: 75-88
• DOI: https://doi.org/10.4028/p-Laoy4G
• Online since: December 2025
• Authors: Abu Bakar Hafeza*, Zakaria Zainal, Heng Jin Tham
• Keywords: Artificial Neural Networks (ANN), Buried Pipelines, Hybrid, Machine Learning

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