Damage assessment in beam-like structures by correlation of spectrum using machine learning
Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2023-07-01
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| Series: | Frattura ed Integrità Strutturale |
| Subjects: | |
| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4310/3847 |
| _version_ | 1797788212067827712 |
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| author | Luan Vuong-Cong Toan Pham-Bao Nhi Ngo-Kieu |
| author_facet | Luan Vuong-Cong Toan Pham-Bao Nhi Ngo-Kieu |
| author_sort | Luan Vuong-Cong |
| collection | DOAJ |
| description | Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation |
| first_indexed | 2024-03-13T01:32:24Z |
| format | Article |
| id | doaj.art-c6ba368cb8e344e78a814eaf1cde0dee |
| institution | Directory Open Access Journal |
| issn | 1971-8993 |
| language | English |
| last_indexed | 2024-03-13T01:32:24Z |
| publishDate | 2023-07-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Frattura ed Integrità Strutturale |
| spelling | doaj.art-c6ba368cb8e344e78a814eaf1cde0dee2023-07-04T07:07:37ZengGruppo Italiano FratturaFrattura ed Integrità Strutturale1971-89932023-07-01176530031910.3221/IGF-ESIS.65.2010.3221/IGF-ESIS.65.20Damage assessment in beam-like structures by correlation of spectrum using machine learningLuan Vuong-CongToan Pham-BaoNhi Ngo-KieuDamage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlationhttps://www.fracturae.com/index.php/fis/article/view/4310/3847damage identificationartificial neural network (ann)decision treespectral correlationbeam-like structure |
| spellingShingle | Luan Vuong-Cong Toan Pham-Bao Nhi Ngo-Kieu Damage assessment in beam-like structures by correlation of spectrum using machine learning Frattura ed Integrità Strutturale damage identification artificial neural network (ann) decision tree spectral correlation beam-like structure |
| title | Damage assessment in beam-like structures by correlation of spectrum using machine learning |
| title_full | Damage assessment in beam-like structures by correlation of spectrum using machine learning |
| title_fullStr | Damage assessment in beam-like structures by correlation of spectrum using machine learning |
| title_full_unstemmed | Damage assessment in beam-like structures by correlation of spectrum using machine learning |
| title_short | Damage assessment in beam-like structures by correlation of spectrum using machine learning |
| title_sort | damage assessment in beam like structures by correlation of spectrum using machine learning |
| topic | damage identification artificial neural network (ann) decision tree spectral correlation beam-like structure |
| url | https://www.fracturae.com/index.php/fis/article/view/4310/3847 |
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