Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring
This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCS...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/3/839 |
| _version_ | 1818514162919145472 |
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| author | Tzu-Kang Lin Yu-Ching Chen |
| author_facet | Tzu-Kang Lin Yu-Ching Chen |
| author_sort | Tzu-Kang Lin |
| collection | DOAJ |
| description | This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy. |
| first_indexed | 2024-12-11T00:11:54Z |
| format | Article |
| id | doaj.art-aaaacd03bf7346479fe3cb91f1b31052 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T00:11:54Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aaaacd03bf7346479fe3cb91f1b310522022-12-22T01:28:08ZengMDPI AGApplied Sciences2076-34172020-01-0110383910.3390/app10030839app10030839Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health MonitoringTzu-Kang Lin0Yu-Ching Chen1Department of Civil Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Civil Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanThis study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.https://www.mdpi.com/2076-3417/10/3/839structural health monitoringartificial neural networkmulti-scale cross-sample entropy |
| spellingShingle | Tzu-Kang Lin Yu-Ching Chen Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring Applied Sciences structural health monitoring artificial neural network multi-scale cross-sample entropy |
| title | Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring |
| title_full | Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring |
| title_fullStr | Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring |
| title_full_unstemmed | Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring |
| title_short | Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring |
| title_sort | integration of refined composite multiscale cross sample entropy and backpropagation neural networks for structural health monitoring |
| topic | structural health monitoring artificial neural network multi-scale cross-sample entropy |
| url | https://www.mdpi.com/2076-3417/10/3/839 |
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