Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks
This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2023/8829298 |
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| _version_ | 1832547932538142720 |
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| author | Daniele Kauctz Monteiro Letícia Fleck Fadel Miguel Gustavo Zeni Tiago Becker Giovanni Souza de Andrade Rodrigo Rodrigues de Barros |
| author_facet | Daniele Kauctz Monteiro Letícia Fleck Fadel Miguel Gustavo Zeni Tiago Becker Giovanni Souza de Andrade Rodrigo Rodrigues de Barros |
| author_sort | Daniele Kauctz Monteiro |
| collection | DOAJ |
| description | This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions. |
| format | Article |
| id | doaj-art-939cffc9f3314e968f4e234e8f168e72 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-939cffc9f3314e968f4e234e8f168e722025-02-03T06:42:55ZengWileyShock and Vibration1875-92032023-01-01202310.1155/2023/8829298Detection, Localization, and Quantification of Damage in Structures via Artificial Neural NetworksDaniele Kauctz Monteiro0Letícia Fleck Fadel Miguel1Gustavo Zeni2Tiago Becker3Giovanni Souza de Andrade4Rodrigo Rodrigues de Barros5Postgraduate Program in Civil Engineering (PPGEC)Department of Mechanical Engineering (DEMEC)Postgraduate Program in Mining, Metallurgy and Materials Engineering (PPGEM)Department of Mechanical Engineering (DEMEC)Applied Mechanics Group (GMAp)Applied Mechanics Group (GMAp)This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.http://dx.doi.org/10.1155/2023/8829298 |
| spellingShingle | Daniele Kauctz Monteiro Letícia Fleck Fadel Miguel Gustavo Zeni Tiago Becker Giovanni Souza de Andrade Rodrigo Rodrigues de Barros Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks Shock and Vibration |
| title | Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks |
| title_full | Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks |
| title_fullStr | Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks |
| title_full_unstemmed | Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks |
| title_short | Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks |
| title_sort | detection localization and quantification of damage in structures via artificial neural networks |
| url | http://dx.doi.org/10.1155/2023/8829298 |
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