PSI - Issue 64

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000–000

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 64 (2024) 661–668

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Vibration-based novelty detection using autoencoders: application to KW51 bridge Marco Pirrò a , Carmelo Gentile a * a DABC, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy In recent years an increasing attention has been given to the application of Artificial Intelligence techniques within the context of Structural Health Monitoring (SHM) of Civil Engineering structures. Multiple advantages compared to traditional techniques based on Operational Modal Analysis (OMA) are achieved, such as a relatively low computational efforts and possible insensitivity to environmental and operational variability (EOV). The present paper proposes an autoencoder-based approach to automatically handle EOVs and, at the same time, to detect structural changes. The proposed procedure is based on the use of an autoencoder (AE) model to reconstruct the measurement data collected during continuous dynamic monitoring. In more details, an AE network is trained using the time series simultaneously collected by all the available channels during a reference period, in which the structure is supposed to be in healthy condition under normal EOV. Through the training procedure, the internal bottleneck layer of AE is supposed to learn how the variation of measured data is affected by the normal EOV. Subsequently, the trained AE is used to reconstruct the data collected from unknown scenarios, providing a reconstruction error (between the measured and the reconstructed signals) that increases as soon as the monitored system departs from its healthy condition. The application of the AE-based procedure is exemplified to the benchmark KW51 bridge (a steel bowstring railway bridge in Leuven, Belgium), showing that the AE network, trained simultaneously with all the available channels, is capable of detecting the structural changes due to a retrofitting performed on the bridge, under changing environmental and operational conditions. © 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 SMAR 2024 Organizers Keywords: SHM; Machine Learning; Environmental effects; railway bridge. © 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 SMAR 2024 Organizers Abstract

* Corresponding author. Tel.: +39 02 2399 4242. E-mail address: carmelo.gentile@polimi.it

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 SMAR 2024 Organizers

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 SMAR 2024 Organizers 10.1016/j.prostr.2024.09.324