Ajinkya Zalkikar
Department of Industrial and System Engineering, Texas A&M University in College Station, TX, USA.

Bimal Nepal
Department of Engineering Technology and Industrial Distribution, Texas A&M University in College Station, TX, USA.

Mani Venkata Rakesh Mutyala
Department of Industrial and Systems Engineering, Texas A&M University in College Station, TX, USA.

Anika Varshney
Department of Industrial and System Engineering, Texas A&M University in College Station, TX, USA.

Lianne Dsouza
Department of Petroleum Engineering, Texas A&M University, College Station, TX, USA.

Hazlina Husin
Department of Petroleum Engineering, University Technology-PETRONAS (UTP), Malaysia.

Om Prakash Yadav
Department of Industrial and Manufacturing Engineering, North Carolina A&T University, Greensboro, NC, USA.

DOI https://doi.org/10.33889/IJMEMS.2025.10.2.016

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Abstract

Natural gas, known for its cleanliness and cost-effectiveness, is transported across vast distances through pipelines. However, the safety concerns that arise from potential ruptures or leaks in these pipelines pose serious threats to the environment and human safety. This paper assesses the reliability of pipelines that have undergone corrosion, compounded by the fluid hammer effect observed in the liquefied gas flow. Machine learning models including support vector machines, linear discriminant analysis, random forest bagging, and Artificial Neural Networks have been meticulously crafted to forecast the safety status of pipelines, considering variables such as the pipe dimensions, material characteristics, fluid velocity, and flow rate. The design of the experiment methodology plays a pivotal role in calculating the pressure surge in pipelines corroded over time due to ongoing corrosion effects. The proposed machine learning models based on simulated data aim to predict the safety status of corroded pipelines with an accuracy rate of up to 97% in controlled environments. Integrating the proposed machine learning models for reliability analysis and pressure surge detection in corroded pipelines, in conjunction with the fluid hammer effect, offers an innovative approach to identifying risks and hazards.

Keywords- Fluid hammer effect, Machine learning, Artificial neural networks, Design of experiments, Pipeline reliability.

Citation

Zalkikar, A., Nepal, B., Mutyala, M. V. R. Varshney, A., Dsouza, L., Husin, H., & Yadav, O. P (2025). Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects. International Journal of Mathematical, Engineering and Management Sciences, 10(2), 285-299. https://doi.org/10.33889/IJMEMS.2025.10.2.016.