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

Weidong Sun, Jialu Zhang, Yasir Mukhtar, Lili Zuo and Shaohua Dong ()
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Weidong Sun: Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
Jialu Zhang: Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
Yasir Mukhtar: Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
Lili Zuo: Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
Shaohua Dong: Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China

Sustainability, 2023, vol. 15, issue 17, 1-13

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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