Novel Data-Driven Framework for Predicting Residual Strength of Corroded Pipelines
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 12, Issue 4
Abstract
For the residual strength prediction of corroded pipelines, the existing standard has a small application range, and the finite-element method has too many assumptions. This paper proposes a new data-driven prediction framework. Firstly, principal component analysis (PCA) is used to reduce the dimensions of the existing data to determine the input-output structure of the prediction model. Secondly, support vector machine (SVM) based on multiobjective optimization is employed to predict the pipeline’s residual strength. Compared with the traditional estimation methods, the model proposed in this paper is data-driven and combines data dimension reduction, multiobjective optimization, and a machine learning model. In addition, the accuracy and stability of the model are considered in the multiobjective optimization. The proposed framework is tested in a pipeline burst pressure data set. The results indicate that the mean absolute percentage error of the proposed models ranges from 1.353% to 3.220%, which has good prediction accuracy and stability. This paper also discusses the influence of the multiobjective optimization algorithm and dimension reduction on the prediction model. The following primary conclusions are drawn: (1) SVM optimized by multiobjective optimizer performs better than SVM optimized by the single-objective optimizer, and the original SVM performs worst, and (2) reducing the raw data dimensions can improve the residual strength prediction performance for corroded pipelines reduce the complexity of the model, and shorten the calculation time.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was financially supported by the National Key Research and Development Program of China with Grant No. 2016YFE0200500, the Program of Changjiang Scholars of Ministry of Education, National Science Fund for Distinguished Young Scholars with Grant No. 51625803, the Fundamental Research Funds for the Central Universities.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 12 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Feb 26, 2021
Accepted: May 12, 2021
Published online: Jul 23, 2021
Published in print: Nov 1, 2021
Discussion open until: Dec 23, 2021
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