Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach
In the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (G...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/7/1241 |
| _version_ | 1797212701856890880 |
|---|---|
| author | Ngoc Dung Bui Minh Dang Tran Hieu Nguyen |
| author_facet | Ngoc Dung Bui Minh Dang Tran Hieu Nguyen |
| author_sort | Ngoc Dung Bui |
| collection | DOAJ |
| description | In the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (GD) for optimal solution searching. In this paper, we introduce an enhanced version of the reptile search algorithm (IRSA), which operates in conjunction with an ANN to mitigate these limitations. By substituting GD with IRSA within an ANN, the network gains the ability to escape local minima, leading to improved prediction outcomes. To demonstrate the efficacy of IRSA in enhancing ANN’s performance, a numerical model of the Nam O Bridge is utilized. This model is updated to closely reflect actual structural conditions. Consequently, damage scenarios for single-element and multielement damage within the bridge structure are developed. The results confirm that ANNIRSA offers greater accuracy than traditional ANNs and ANNRSAs in predicting structural damage. |
| first_indexed | 2024-04-24T10:46:34Z |
| format | Article |
| id | doaj.art-5b2f6a5d6575478bbefce224596059f7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T10:46:34Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5b2f6a5d6575478bbefce224596059f72024-04-12T13:17:10ZengMDPI AGElectronics2079-92922024-03-01137124110.3390/electronics13071241Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA ApproachNgoc Dung Bui0Minh Dang1Tran Hieu Nguyen2Faculty of Information Technology, University of Transport and Communications, Hanoi 100000, VietnamInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamFaculty of Information Technology, University of Transport and Communications, Hanoi 100000, VietnamIn the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (GD) for optimal solution searching. In this paper, we introduce an enhanced version of the reptile search algorithm (IRSA), which operates in conjunction with an ANN to mitigate these limitations. By substituting GD with IRSA within an ANN, the network gains the ability to escape local minima, leading to improved prediction outcomes. To demonstrate the efficacy of IRSA in enhancing ANN’s performance, a numerical model of the Nam O Bridge is utilized. This model is updated to closely reflect actual structural conditions. Consequently, damage scenarios for single-element and multielement damage within the bridge structure are developed. The results confirm that ANNIRSA offers greater accuracy than traditional ANNs and ANNRSAs in predicting structural damage.https://www.mdpi.com/2079-9292/13/7/1241damaged detectionstructural health monitoringANNRSAIRSA |
| spellingShingle | Ngoc Dung Bui Minh Dang Tran Hieu Nguyen Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach Electronics damaged detection structural health monitoring ANN RSA IRSA |
| title | Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach |
| title_full | Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach |
| title_fullStr | Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach |
| title_full_unstemmed | Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach |
| title_short | Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach |
| title_sort | damage detection in structural health monitoring using an integrated annirsa approach |
| topic | damaged detection structural health monitoring ANN RSA IRSA |
| url | https://www.mdpi.com/2079-9292/13/7/1241 |
| work_keys_str_mv | AT ngocdungbui damagedetectioninstructuralhealthmonitoringusinganintegratedannirsaapproach AT minhdang damagedetectioninstructuralhealthmonitoringusinganintegratedannirsaapproach AT tranhieunguyen damagedetectioninstructuralhealthmonitoringusinganintegratedannirsaapproach |