TITLE:
AI Assisted Material Selection Framework for Corrosion Resistant Steels in Onshore Oil and Gas Pipelines
AUTHORS:
Stephen Oluwatosin Okegbenro, Opeyemi Bosede Daniyan, Kunle Michael Oluwasegun, Olakanmi Adekunle Adewara, Bodunde Odunola Akinyeyemi, Ganiyu Adesola Aderounmu
KEYWORDS:
Corrosion, Pipelines, Machine Learning, Artificial Intelligence
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.1,
January
27,
2026
ABSTRACT: Corrosion is one of the most challenging problems that affects the safety and durability of onshore pipelines. Corrosion-resistant steels play a pivotal role in ensuring long lasting function, cost reduction in upkeep, and prevention of major breakdowns. The traditional materials selection methods often depend on hands-on testing, expert experience and the use of a trial-and-error process. The rapid advancement of machine learning and artificial intelligence (AI) driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various domains, including materials design and discovery. This paper aims to develop and facilitate how AI is used to interpret Ashby’s chart and merge databases of materials together to select materials for corrosion resistant steels in onshore oil and gas pipelines. This paper proposes an AI Assisted material selection framework specifically designed to identify optimal corrosion resistant steels for onshore pipelines oil and gas pipelines by improving forecast accuracy, reducing uncertainty, enabling informed material selection, and strengthening longterm pipeline integrity in corrosive operating environments. By leveraging machine learning (ML) techniques such as Gradient Boosting Machines (GBM), Principal Component Analysis (PCA) for dimensionality reduction, and Neural Networks. The framework intelligently analyzes vast datasets encompassing operational parameters, environmental conditions, and historical corrosion rates.