Work

Machine learning methods for predicting residual strength in corroded oil and gas steel pipes

Year: 2025

Type: article

Abstract: This review examines machine learning approaches for predicting pipeline residual strength, which is crucial for assessing operational lifespan and safety in industrial applications. We analyze various machine learning models, data preprocessing methods, and evaluation metrics used in existing research. The study highlights how data characteristics and model selection influence prediction accuracy, providing practitioners with guidelines for model implementation, while discussing current challenges and future research directions.

Source: npj Materials Degradation

DOI: 10.1038/s41529-025-00573-y

Authors: Qiankun Wang, Hongfang Lu

Institutions: China University of Geosciences (Beijing), Chinese Academy of Sciences, Ningbo Institute of Industrial Technology, Southeast University

Language: English

Topic: Structural Integrity and Reliability Analysis

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,590