publications

Evolution of corrosion prediction models for oil and gas pipelines: From empirical-driven to data-driven

Published in Engineering Failure Analysis, 2023

This work systematically reviews these models including their evolution, characteristics, limitations, and performance, and highlights the application of data-driven models. In addition, a ML method database of corrosion prediction for oil and gas pipelines was created by summarizing the pre-processing, input and output parameters and performance metrics of ML models, which provide guidance for rational selection of models. Finally, conclusions and recommendations are presented and provide a broad outlook for future research paths.

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Interpretable machine learning for maximum corrosion depth and influence factor analysis

Published in npj Materials Degradation, 2023

This paper is about prediction of pitting depth, which uses interpretable machine learning methods to explore the running of the model and the laws of effect of features.

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a continuation …

Published in Journal 1, 2009

This paper is about the number 1. The number 2 is left for future work.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf