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Volume 2019, Article ID 3429816, 15 pages
https://doi.org/10.1155/2019/3429816
Research Article

Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network

1School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
2Department of Intelligent Equipment, Changzhou College of Information Technology, Changzhou, Jiangsu 213164, China
3College of Internet of Things Engineering, Hohai University, Changzhou 213022, China

Correspondence should be addressed to Wei Li; moc.oohay@215tmuceemc

Received 20 May 2019; Revised 26 August 2019; Accepted 16 September 2019; Published 31 October 2019

Academic Editor: Qingling Wang

Copyright © 2019 Chengtao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The buried pipelines and metallic structures in subway systems are subjected to electrochemical corrosion under the stray current interference. The corrosion current density determines the degree and the speed of stray current corrosion. A method combining electrochemical experiment with the machine learning algorithm was utilized in this research to study the corrosion current density under the coupling action of stray current and chloride ion. In this study, a quantum particle swarm optimization-neural network (QPSO-NN) model was built up to predict the corrosion current density in the process of stray current corrosion. The QPSO algorithm was employed to optimize the updating process of weights and biases in the artificial neural network (ANN). The results show that the accuracy of the proposed QPSO-NN model is better than the model based on backpropagation neural network (BPNN) and particle swarm optimization-neural network (PSO-NN). The accuracy distribution of the QPSO-NN model is more stable than that of the BPNN model and the PSO-NN model. The presented model can be used for the prediction of corrosion current density and provides the possibility to monitor the stray current corrosion in subway system through an intelligent learning algorithm.