PSI - Issue 62

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Procedia Structural Integrity 62 (2024) 879–886

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II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) AI-driven Automated Operational Modal Analysis of Bridges Israel Alejandro Hernández-González a , Enrique García-Macías a* , Gabriel Constante b , Filippo Ubertini c a Department of Structural Mechanics and Hydraulic Engineering, University of Granada, Campus de Fuentenueva s/n, 18071 Granada, Spain. b Department of Engineering, University of Perugia, Via G. Duranti, 93 - 06125 Perugia, Italy. c Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti, 93 - 06125 Perugia, Italy. Abstract The growing awareness on the daunting challenge posed by ageing civil infrastructure is fostering the increasingly frequent implementation of dense Structural Health Monitoring (SHM) systems. The Italian context represents a formidable example of this trend, especially after the tragic collapse of the Morandi Bridge in 2018 and the release of the National Guidelines on the Assessment and Management of the Risk Condition of Bridges and Viaducts in 2020. This has motivated the implementation of numerous monitoring systems all throughout the national territory, many of them involving heterogeneous and dense sensor networks. The implementation of such a vast number of sensors presents new challenges at the management and decision-making levels, particularly in the signal processing and feature extraction phases. This is particularly critical for vibration-based SHM techniques, in which classical operational modal analysis (OMA) techniques are proving inefficient for handling such large monitoring databases. As an attempt to alleviate these limitations, this contribution presents the development of an AI-driven OMA technique for rapid identification of bridges. The proposed methodology consists of a multi-task deep feedforward neural network capable of extracting the independent modal components from ambient vibration records. Trained in a supervised manner by a Second Order Blind Source Identification (SOBI) method, the developed AI model can extract both the real and complex independent modal components. The effectiveness of the presented approach is illustrated through of a real-world-bridge, the Méndez-Núñez Bridge in Granada, Spain, demonstrating great potential as a computationally efficient OMA technique for on-site edge modal identification of bridges. II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) AI-driven Automated Operational Modal Analysis of Bridges Israel Alejandro Hernández-González a , Enrique García-Macías a* , Gabriel Constante b , Filippo Ubertini c a Department of Structural Mechanics and Hydraulic Engineering, University of Granada, Campus de Fuentenueva s/n, 18071 Granada, Spain. b Department of Engineering, University of Perugia, Via G. Duranti, 93 - 06125 Perugia, Italy. c Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti, 93 - 06125 Perugia, Italy. Abstract The growing awareness on the daunting challenge posed by ageing civil infrastructure is fostering the increasingly frequent implementation of dense Structural Health Monitoring (SHM) systems. The Italian context represents a formidable example of this trend, especially after the tragic collapse of the Morandi Bridge in 2018 and the release of the National Guidelines on the Assessment and Management of the Risk Condition of Bridges and Viaducts in 2020. This has motivated the implementation of numerous monitoring systems all throughout the national territory, many of them involving heterogeneous and dense sensor networks. The implementation of such a vast number of sensors presents new challenges at the management and decision-making levels, particularly in the signal processing and feature extraction phases. This is particularly critical for vibration-based SHM techniques, in which classical operational modal analysis (OMA) techniques are proving inefficient for handling such large monitoring databases. As an attempt to alleviate these limitations, this contribution presents the development of an AI-driven OMA technique for rapid identification of bridges. The proposed methodology consists of a multi-task deep feedforward neural network capable of extracting the independent modal components from ambient vibration records. Trained in a supervised manner by a Second Order Blind Source Identification (SOBI) method, the developed AI model can extract both the real and complex independent modal components. The effectiveness of the presented approach is illustrated through of a real-world-bridge, the Méndez-Núñez Bridge in Granada, Spain, demonstrating great potential as a computationally efficient OMA technique for on-site edge modal identification of bridges. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Scientific Board Members Keywords: "Artifcial Intelligence; Blind Source Separation; Deep Neural Networks; Operational Modal Analysis; Structural Health Monitoring;"

Keywords: "Artifcial Intelligence; Blind Source Separation; Deep Neural Networks; Operational Modal Analysis; Structural Health Monitoring;"

* Corresponding author. Tel.: +34 958241000 (Ext. 20668). E-mail address: enriquegm@ugr.es

* Corresponding author. Tel.: +34 958241000 (Ext. 20668). E-mail address: enriquegm@ugr.es

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Scientific Board Members 10.1016/j.prostr.2024.09.118 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4. 0 ) Peer-review under responsibility of Scientific Board Member s 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4. 0 ) Peer-review under responsibility of Scientific Board Member s