Review on automated condition assessment of pipelines with machine learning.

[Display omitted] • Automated condition assessment of pipelines is achieved by machine learning models. • Detecting, locating, and quantifying anomalies of pipelines are reviewed. • Operation data, nondestructive testing data, and computer vision data are covered. • SWOT analysis and practical recommendations are discussed. Pipelines carrying energy products play vital roles in economic wealth and public safety, but incidents continue occurring. Condition assessment of pipelines is essential to identify anomalies timely. Advanced sensing technologies obtain informative data for condition assessment, while data analysis by human has limited efficiency, accuracy, and reliability. Advances in machine learning offer exciting opportunities for automated condition assessment with minimum human intervention. This paper reviews machine learning approaches to detect, classify, locate, and quantify pipeline anomalies based on intelligent interpretation of routine operation data, nondestructive testing data, and computer vision data. Statistics and uncertainties of performance metrics of machine learning approaches are discussed. An analysis on strengths, weaknesses, opportunities, and threats (SWOT) is performed. Guides for practitioners to perform automated pipeline condition assessment are recommended. This review provide insights into the machine learning approaches for automated pipeline condition assessment. The SWOT analysis will support decision making in the pipeline industry. [ABSTRACT FROM AUTHOR]