Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations

Abstract

The use of artificial intelligence (AI) in gas pipeline monitoring and maintenance represents a significant advancement in the energy industry. This article provides an overview of current trends and AI technologies applied in fault detection, failure prediction, and gas transportation optimization. Key methods include machine learning, deep neural networks, numerical simulations, and digital twins. Research highlights the importance of integrating AI with the physical properties of materials for localizing and assessing corrosion defects. A bibliometric analysis reveals that most studies focus on the application of neural networks, support vector machines, and Bayesian networks in predictive maintenance. Despite significant progress, challenges remain, such as the lack of high-quality datasets, high implementation costs, and regulatory barriers. Future research trends focus on the integration of AI with SCADA systems, improving predictive models, and the broader use of generative neural networks for data synthesis. This review of research trends from 2020 to 2025 underscores the importance of artificial intelligence in the transportation sector and highlights its potential for further development in enhancing the reliability and safety of energy infrastructures.