Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Assessment of Structures Using Dynamic Monitoring and Auto-Encoders: Application to Baixo Sabor Dam
https://orcid.org/http://orcid.org/0000-0001-8334-3620
https://orcid.org/http://orcid.org/0000-0002-9544-2762
https://orcid.org/http://orcid.org/0000-0002-3260-8243
https://orcid.org/http://orcid.org/0000-0001-7878-6224
An increasing attention has been recently payed to Artificial Intelligence (AI) techniques in Structural Health Monitoring (SHM) of Civil Engineering structures. When compared to traditional OMA-based techniques, AI exhibits the advantage of relatively low computational efforts, not requiring any system identification, as well as the implicit modeling of environmental and operational variability (EOV). Within the context of vibration-based SHM, the present paper proposes a procedure based on Auto-Encoder (AE) network for detection and localization of structural changes In more details, the data simultaneously collected by all available data channels are used to define an AE network in a training period during which, as usual, the monitored system is supposed to be in healthy condition under normal EOVs. Once trained, the network is used to reconstruct the newly collected data, with the mean reconstruction error (between the measured and the reconstructed signals) increasing as soon as the monitored system departs from healthy condition. The application of the presented procedure is exemplified to data measured in Baixo Sabor dam (Portugal) under varying temperature, water level of the reservoir and operating conditions of the turbine.
Assessment of Structures Using Dynamic Monitoring and Auto-Encoders: Application to Baixo Sabor Dam
https://orcid.org/http://orcid.org/0000-0001-8334-3620
https://orcid.org/http://orcid.org/0000-0002-9544-2762
https://orcid.org/http://orcid.org/0000-0002-3260-8243
https://orcid.org/http://orcid.org/0000-0001-7878-6224
An increasing attention has been recently payed to Artificial Intelligence (AI) techniques in Structural Health Monitoring (SHM) of Civil Engineering structures. When compared to traditional OMA-based techniques, AI exhibits the advantage of relatively low computational efforts, not requiring any system identification, as well as the implicit modeling of environmental and operational variability (EOV). Within the context of vibration-based SHM, the present paper proposes a procedure based on Auto-Encoder (AE) network for detection and localization of structural changes In more details, the data simultaneously collected by all available data channels are used to define an AE network in a training period during which, as usual, the monitored system is supposed to be in healthy condition under normal EOVs. Once trained, the network is used to reconstruct the newly collected data, with the mean reconstruction error (between the measured and the reconstructed signals) increasing as soon as the monitored system departs from healthy condition. The application of the presented procedure is exemplified to data measured in Baixo Sabor dam (Portugal) under varying temperature, water level of the reservoir and operating conditions of the turbine.
Assessment of Structures Using Dynamic Monitoring and Auto-Encoders: Application to Baixo Sabor Dam
https://orcid.org/http://orcid.org/0000-0001-8334-3620
https://orcid.org/http://orcid.org/0000-0002-9544-2762
https://orcid.org/http://orcid.org/0000-0002-3260-8243
https://orcid.org/http://orcid.org/0000-0001-7878-6224
An increasing attention has been recently payed to Artificial Intelligence (AI) techniques in Structural Health Monitoring (SHM) of Civil Engineering structures. When compared to traditional OMA-based techniques, AI exhibits the advantage of relatively low computational efforts, not requiring any system identification, as well as the implicit modeling of environmental and operational variability (EOV). Within the context of vibration-based SHM, the present paper proposes a procedure based on Auto-Encoder (AE) network for detection and localization of structural changes In more details, the data simultaneously collected by all available data channels are used to define an AE network in a training period during which, as usual, the monitored system is supposed to be in healthy condition under normal EOVs. Once trained, the network is used to reconstruct the newly collected data, with the mean reconstruction error (between the measured and the reconstructed signals) increasing as soon as the monitored system departs from healthy condition. The application of the presented procedure is exemplified to data measured in Baixo Sabor dam (Portugal) under varying temperature, water level of the reservoir and operating conditions of the turbine.
Assessment of Structures Using Dynamic Monitoring and Auto-Encoders: Application to Baixo Sabor Dam
Lecture Notes in Civil Engineering
Rainieri, Carlo (Herausgeber:in) / Gentile, Carmelo (Herausgeber:in) / Aenlle López, Manuel (Herausgeber:in) / Pirrò, Marco (Autor:in) / Pereira, Sérgio (Autor:in) / Gentile, Carmelo (Autor:in) / Cunha, Álvaro (Autor:in)
International Operational Modal Analysis Conference ; 2024 ; Naples, Italy
Proceedings of the 10th International Operational Modal Analysis Conference (IOMAC 2024) ; Kapitel: 28 ; 281-291
23.06.2024
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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