A new hybrid approach model for predicting burst pressure of corroded pipelines of gas and oil. (July 2023)

• Record Type: Journal Article
• Authors: Ma, Haonan; Wang, Hantong; Geng, Mengying; Ai, Yibo; Zhang, Weidong; Zheng, Wenyue
• Is Part Of: Engineering failure analysis. Volume 149(2023)
• Publication Date: 2023-07
• Subjects: Ensemble learning -- Empirical formulas -- Burst pressure -- Corrode pipeline -- LightGBM -- Tree-structured Parzen Estimator; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2023.107248
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: This paper presents a hybrid approach model for predicting the burst pressure of corroded pipelines. A new feature space with physical meaning is constructed. The proposed fusion mechanism makes full use of the prior knowledge in the Tresca criterion and the predictive ability of ensemble learning. LightGBM is used to build a predictive model, and the Tree-structured Parzen Estimator algorithm is used for hyper-parameter search. This paper establishes a burst pressure dataset of full-scale corroded pipelines ranging from low strength to high strength. Abstract: Accurate prediction of the burst pressure of corroded pipeline is of great significance to pipeline design, reliability analysis and maintenance decision. This paper presents a novel hybrid approach model for predicting the burst pressure of corroded pipelines. First, a new feature space with physical meaning is constructed based on the pipeline geometry, the size of corrosion defects, and the mechanical properties of materials. Then, a fusion mechanism combining empirical formula and ensemble learning is proposed, which makes full use of the prior knowledge in the Tresca criterion and the predictive ability of ensemble learning. The prediction model is built using the Light Gradient Boosting Machine, and the hyper-parameters are tuned using the Tree-structured Parzen Estimator algorithm. Finally, a burst pressure dataset of corroded full-scale oil and gas pipelines ranging from low strength to high strength is established to verify this model. The prediction results demonstrate that the hybrid approach model can significantly improve the prediction accuracy. And compared to other ensemble learning methods such as random forest and XGBoost, the accuracy of this model proposed in this paper is the highest. The correlation coefficient is 0.98163, the mean square error is 0.98087 MPa, the mean absolute error is 0.66500 MPa, and mean absolute percentage error is 0.04480. The model also provides feature importance, enhancing the interpretability of the model. In addition, further experiments demonstrate that this model has good adaptability to corroded pipelines of different strengths grade and obvious advantages compared with the calculation results of five traditional empirical formulas.