Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

Sun, Weidong; Zhang, Jialu; Mukhtar, Yasir; Zuo, Lili; Dong, Shaohua

doi:10.3390/su151712724

Open AccessArticle

Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

by

Weidong Sun

^1,2,

Jialu Zhang

¹,

Yasir Mukhtar

^1,3

,

Lili Zuo

¹

and

Shaohua Dong

^1,3,*

¹

Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China

²

Pipe Network Group (Xuzhou) Pipeline Inspection and Testing Co., Ltd., Xuzhou 221008, China

³

Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China

^*

Author to whom correspondence should be addressed.

Sustainability 2023, 15(17), 12724; https://doi.org/10.3390/su151712724

Submission received: 9 May 2023 / Revised: 1 August 2023 / Accepted: 6 August 2023 / Published: 23 August 2023

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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Abstract

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline.

Keywords: submarine pipeline; safety factor; analytic hierarchy process; radial basis neural network

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MDPI and ACS Style

Sun, W.; Zhang, J.; Mukhtar, Y.; Zuo, L.; Dong, S. Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network. Sustainability 2023, 15, 12724. https://doi.org/10.3390/su151712724

AMA Style

Sun W, Zhang J, Mukhtar Y, Zuo L, Dong S. Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network. Sustainability. 2023; 15(17):12724. https://doi.org/10.3390/su151712724

Chicago/Turabian Style

Sun, Weidong, Jialu Zhang, Yasir Mukhtar, Lili Zuo, and Shaohua Dong. 2023. "Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network" Sustainability 15, no. 17: 12724. https://doi.org/10.3390/su151712724

APA Style

Sun, W., Zhang, J., Mukhtar, Y., Zuo, L., & Dong, S. (2023). Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network. Sustainability, 15(17), 12724. https://doi.org/10.3390/su151712724

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

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