Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection
Bridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5187 |
| _version_ | 1797596779913412608 |
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| author | Sardorbek Niyozov Marco Domaneschi Joan R. Casas Rick M. Delgadillo |
| author_facet | Sardorbek Niyozov Marco Domaneschi Joan R. Casas Rick M. Delgadillo |
| author_sort | Sardorbek Niyozov |
| collection | DOAJ |
| description | Bridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge interaction. In detail, the current study presents an approach to temperature removal in the case of forced vibrations in the bridge using principal component analysis, with detection and localization of damage using an unsupervised machine learning algorithm. Due to the difficulty in obtaining real data on undamaged and later damaged bridges that are simultaneously influenced by traffic and temperature changes, the proposed method is validated using a numerical bridge benchmark. The vertical acceleration response is derived from a time-history analysis with a moving load under different ambient temperatures. The results show how machine learning algorithms applied to bridge damage detection appear to be a promising technique to efficiently solve the problem’s complexity when both operational and environmental variability are included in the recorded data. However, the example application still shows some limitations, such as the use of a numerical bridge and not a real bridge due to the lack of vibration data under health and damage conditions, and with varying temperatures; the simple modeling of the vehicle as a moving load; and the crossing of only one vehicle present in the bridge. This will be considered in future studies. |
| first_indexed | 2024-03-11T02:56:53Z |
| format | Article |
| id | doaj.art-9c791a61312341ac9aa4d2e208533f46 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:56:53Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9c791a61312341ac9aa4d2e208533f462023-11-18T08:33:47ZengMDPI AGSensors1424-82202023-05-012311518710.3390/s23115187Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage DetectionSardorbek Niyozov0Marco Domaneschi1Joan R. Casas2Rick M. Delgadillo3Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainDepartment of Civil Engineering, Universidad de Ingenieria y Tecnologia—UTEC, Jr. Medrano Silva 165, Barranco, Lima 15063, PeruBridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge interaction. In detail, the current study presents an approach to temperature removal in the case of forced vibrations in the bridge using principal component analysis, with detection and localization of damage using an unsupervised machine learning algorithm. Due to the difficulty in obtaining real data on undamaged and later damaged bridges that are simultaneously influenced by traffic and temperature changes, the proposed method is validated using a numerical bridge benchmark. The vertical acceleration response is derived from a time-history analysis with a moving load under different ambient temperatures. The results show how machine learning algorithms applied to bridge damage detection appear to be a promising technique to efficiently solve the problem’s complexity when both operational and environmental variability are included in the recorded data. However, the example application still shows some limitations, such as the use of a numerical bridge and not a real bridge due to the lack of vibration data under health and damage conditions, and with varying temperatures; the simple modeling of the vehicle as a moving load; and the crossing of only one vehicle present in the bridge. This will be considered in future studies.https://www.mdpi.com/1424-8220/23/11/5187bridge damage detectionnon-stationaryPCAK-meansoperational-environmental variability |
| spellingShingle | Sardorbek Niyozov Marco Domaneschi Joan R. Casas Rick M. Delgadillo Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection Sensors bridge damage detection non-stationary PCA K-means operational-environmental variability |
| title | Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection |
| title_full | Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection |
| title_fullStr | Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection |
| title_full_unstemmed | Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection |
| title_short | Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection |
| title_sort | temperature effects removal from non stationary bridge vehicle interaction signals for ml damage detection |
| topic | bridge damage detection non-stationary PCA K-means operational-environmental variability |
| url | https://www.mdpi.com/1424-8220/23/11/5187 |
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