Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3429816 |
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| author | Chengtao Wang Wei Li Gaifang Xin Yuqiao Wang Shaoyi Xu |
| author_facet | Chengtao Wang Wei Li Gaifang Xin Yuqiao Wang Shaoyi Xu |
| author_sort | Chengtao Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5a9d2d98cec24ec9b6bd4f2d6ddaeac4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5a9d2d98cec24ec9b6bd4f2d6ddaeac42025-02-03T01:22:00ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/34298163429816Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural NetworkChengtao Wang0Wei Li1Gaifang Xin2Yuqiao Wang3Shaoyi Xu4School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaDepartment of Intelligent Equipment, Changzhou College of Information Technology, Changzhou, Jiangsu 213164, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaThe 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.http://dx.doi.org/10.1155/2019/3429816 |
| spellingShingle | Chengtao Wang Wei Li Gaifang Xin Yuqiao Wang Shaoyi Xu Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network Complexity |
| title | Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network |
| title_full | Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network |
| title_fullStr | Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network |
| title_full_unstemmed | Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network |
| title_short | Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network |
| title_sort | prediction model of corrosion current density induced by stray current based on qpso driven neural network |
| url | http://dx.doi.org/10.1155/2019/3429816 |
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