Research on Failure Pressure Prediction of Water Supply Pipe Based on GA-BP Neural Network

• Published in: Water (Basel) Vol. 16; no. 18; p. 2659
• Main Authors: Li, Qingfu, Li, Zeyi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; Age; Algorithms; Artificial intelligence; Back propagation; Cathodic protection; Comparative analysis; Corrosion; Decision trees; Equipment and supplies; Failure; Finite element analysis; Fracture mechanics; Generalized linear models; Genetic algorithms; Logistics; Maintenance and repair; Methods; Neural networks; Optimization; Pipes; prediction; Propagation; Regression analysis; Risk assessment; Simulation; Soils; Testing; topology; Variables; water; Water supply; Water-pipes; China
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w16182659

The water supply pipeline is regarded as the “lifeline” of the city. In recent years, pipeline accidents caused by aging and other factors are common and have caused large economic losses. Therefore, in order to avoid large economic losses, it is necessary to analyze the failure prediction of pipelines so that the pipelines that are going to fail can be replaced in a timely manner. In this paper, we propose a method for predicting the failure pressure of pipelines, i.e., a genetic algorithm was used to optimize the weights and thresholds of a BP neural network. The first step was to determine the topology of the neural network and the number of input and output variables. The second step was to optimize the weights and thresholds initially set for the back propagation neural network using a genetic algorithm. Finally, the optimized back-propagation neural network was used to simulate and predict pipeline failures. It was proved by examples that compared with the separate back propagation neural network model and the optimized and trained genetic algorithm-back propagation neural network, the model performed better in simulation prediction, and the prediction accuracy could reach up to 91%, whereas the unoptimized back propagation neural network model could only reach 85%. It is feasible to apply this model for fault prediction of pipelines.