- Title
- Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure
- Creator
- Cai, Jie; Jiang, Xiaoli; Yang, Yazhou; Lodewijks, Gabriel; Wang, Minchang
- Relation
- Journal of Marine Science and Application Vol. 21, Issue 2, p. 115-132
- Publisher Link
- http://dx.doi.org/10.1007/s11804-022-00263-0
- Publisher
- Ha'erbin Gongcheng Daxue,Ha'erbin Engineering University
- Resource Type
- journal article
- Date
- 2022
- Description
- 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.
- Subject
- pipelines; corrosion; burst strength; internal pressure; data-driven method; machine learning
- Identifier
- http://hdl.handle.net/1959.13/1489340
- Identifier
- uon:52686
- Identifier
- ISSN:1671-9433
- Language
- eng
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