Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge
Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7484 |
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| author | Minh Q. Tran Hélder S. Sousa Thuc V. Ngo Binh D. Nguyen Quyen T. Nguyen Huan X. Nguyen Edward Baron José Matos Son N. Dang |
| author_facet | Minh Q. Tran Hélder S. Sousa Thuc V. Ngo Binh D. Nguyen Quyen T. Nguyen Huan X. Nguyen Edward Baron José Matos Son N. Dang |
| author_sort | Minh Q. Tran |
| collection | DOAJ |
| description | Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents. |
| first_indexed | 2024-03-11T01:47:56Z |
| format | Article |
| id | doaj.art-43046f581bba4b00a8fe133c89f51be6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:56Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-43046f581bba4b00a8fe133c89f51be62023-11-18T16:07:07ZengMDPI AGApplied Sciences2076-34172023-06-011313748410.3390/app13137484Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss BridgeMinh Q. Tran0Hélder S. Sousa1Thuc V. Ngo2Binh D. Nguyen3Quyen T. Nguyen4Huan X. Nguyen5Edward Baron6José Matos7Son N. Dang8ISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, PortugalISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, PortugalInstitute of Science and International Cooperation, Mien Tay Construction University, Vĩnh Long 85100, VietnamISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, Portugal2C2T-Centro de Ciência e Tecnologia Têxtil, Universidade do Minho, 4800-058 Guimarães, PortugalFaculty of Science and Technology, Middlesex University, London NW4 4BT, UKISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, PortugalISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, PortugalISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, PortugalDamage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.https://www.mdpi.com/2076-3417/13/13/7484ANNFEMdamage assessmentstructural health monitoringsteel truss bridge |
| spellingShingle | Minh Q. Tran Hélder S. Sousa Thuc V. Ngo Binh D. Nguyen Quyen T. Nguyen Huan X. Nguyen Edward Baron José Matos Son N. Dang Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge Applied Sciences ANN FEM damage assessment structural health monitoring steel truss bridge |
| title | Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge |
| title_full | Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge |
| title_fullStr | Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge |
| title_full_unstemmed | Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge |
| title_short | Structural Assessment Based on Vibration Measurement Test Combined with an Artificial Neural Network for the Steel Truss Bridge |
| title_sort | structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge |
| topic | ANN FEM damage assessment structural health monitoring steel truss bridge |
| url | https://www.mdpi.com/2076-3417/13/13/7484 |
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