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Hydraulic transients pose a significant threat to pipeline integrity, leading to catastrophic failures from pressure surges. Traditional methods for selecting protection devices – such as air vessels and surge tanks – often rely on engineering judgment, potentially leading to suboptimal solutions. This study introduces a data-driven approach using artificial neural networks (ANNs) to objectively select the most suitable protection devices, overcoming the limitations of conventional engineering intuition. A comprehensive dataset, representing diverse pipeline configurations and commercial materials, was developed. Utilizing established selection criteria, we identified optimal protection devices for various scenarios. Four distinct ANN architectures were trained and assessed based on performance metrics such as accuracy and precision, with the best model validated using an independent dataset of previously unseen configurations. The trained ANN model demonstrated 91.5% accuracy in device selection, outperforming traditional methods and offering enhanced strategies for pipeline protection across diverse scenarios. By incorporating a broader range of pipeline configurations and physical factors, the proposed ANN-based approach offers a robust tool for optimizing pipeline protection strategies and transcends engineer intuition, potentially revealing unconventional yet highly effective solution.

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  • Innovative use of artificial neural networks (ANNs) for selecting pipeline protection devices, achieving 91.5% accuracy.

  • Comprehensive dataset encompassing hydraulic variables and terrain profiles to optimize device selection.

  • Optimized ANN architecture with a single hidden layer of 200 neurons successfully.

  • Validation with an independent dataset yielded a 94% success rate, confirming the model's reliability.

air vessel, artificial neural networks (ANNs), hydraulic transients, pipeline integrity, protection devices, surge tank
© 2025 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).