Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects

• Published in: International journal of mathematical, engineering and management sciences Vol. 10; no. 2; pp. 285 - 299
• Main Authors: Zalkikar, Ajinkya, Nepal, Bimal, Mutyala, Mani Venkata Rakesh, Varshney, Anika, Dsouza, Lianne, Husin, Hazlina, Yadav, Om Prakash
• Format: Journal Article
• Language: English
• Published: Dehradun International Journal of Mathematical, Engineering and Management Sciences 01.04.2025; Ram Arti Publishers
• Subjects: artificial neural networks; Corrosion; design of experiments; fluid hammer effect; Machine learning; pipeline reliability; Pipelines

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 reliabi...