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

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  • Chengtao Wang
  • Wei Li
  • Gaifang Xin
  • Yuqiao Wang
  • Shaoyi Xu

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.

Suggested Citation

  • Chengtao Wang & Wei Li & Gaifang Xin & Yuqiao Wang & Shaoyi Xu, 2019. "Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    • Handle: RePEc:hin:complx:3429816
    DOI: 10.1155/2019/3429816
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    References listed on IDEAS

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    1. Dann, Markus R. & Maes, Marc A., 2018. "Stochastic corrosion growth modeling for pipelines using mass inspection data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 245-254.
    2. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
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