Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

Cai, Jie (University of Southern Denmark)
Jiang, X. (TU Delft Transport Engineering and Logistics)
Yang, Yazhou (National University of Defense Technology)
Lodewijks, Gabriel (The University of Newcastle, Australia)
Wang, Minchang (Hangzhou Silan Microelectronics Co. Ltd.)

2022

A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines, especially those served for a long time. Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion. However, it is time-consuming for finite-element method and there is a limited application range by using empirical formulas. In order to improve the prediction of strength, this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods. Three supervised ML (machine learning) algorithms, including the ANN (artificial neural network), the SVM (support vector machine) and the LR (linear regression), are deployed to train models based on experimental data. Data analysis is first conducted to determine proper pipe features for training. Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines. Among all the proposed data-driven models, the ANN model with three neural layers has the highest training accuracy, but also presents the largest variance. The SVM model provides both high training accuracy and high validation accuracy. The LR model has the best performance in terms of generalization ability. These models can be served as surrogate models by transfer learning with new coming data in future research, facilitating a sustainable and intelligent decision-making of corroded pipelines.

Burst strength
Corrosion
Data-driven method
Internal pressure
Machine learning
Pipelines

http://resolver.tudelft.nl/uuid:f20e0931-1a72-4ead-8f19-298ef157822f

https://doi.org/10.1007/s11804-022-00263-0

2023-01-29

1671-9433

Journal of Marine Science and Application, 21 (2), 115-132

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Jie Cai, X. Jiang, Yazhou Yang, Gabriel Lodewijks, Minchang Wang