PSI - Issue 62

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

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 62 (2024) 887–894

II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) On the application of domain adaptation for knowledge transfer and damage detection across bridge spans: an experimental case study Valentina Giglioni a *, Jack Poole b , Robin Mills b , Ilaria Venanzi a , Filippo Ubertini a , Keith Worden b a Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, Perugia-06125, Italy b Dynamic Research group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, S1 3JD Sheffield, United Kingdom Abstract Machine Learning offers a substantial support for vibration-based Structural Health Monitoring and automatic damage assessment in real-time. However, labelled health-state data are often limited and difficult to collect, especially under various environmental conditions and damage scenarios. To bridge this gap, Population-Based Structural Health Monitoring is employed to enrich the available dataset by acquiring information from a population of similar structures and to afterwards develop Transfer Learning strategies across different structural systems. In this field, a major challenge is that the development and validation of robust techniques require a large dataset from multiple real structures covering a wide range of health-states. To this end, the present paper firstly illustrates an experimental campaign conducted on a laboratory-scale bridge model to create a comprehensive dataset using different bridge configurations, that are subjected to various environmental conditions and simulated damage scenarios. It follows that the described population certainly provides the basis to investigate TL and gain insights into the field of PBSHM. Working on this case-study, a new application of Domain Adaptation is proposed, with the aim to perform damage identification via label sharing across different spans of a continuously monitored bridge. The extracted natural frequencies are projected into a latent feature space and a machine learning algorithm is then adopted to recognise a certain damage in one span, based on the information previously learned on a different bridge span. This approach is useful if considering that multiple portions could be reasonably affected by similar defects. Therefore, the proposed approach overcomes the limitations of conventional ML-based SHM methods, paving the way to facilitate risk assessment in transportation networks and identify similar anomalies by exchanging span-related health-state information. © 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 © 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

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 Members 10.1016/j.prostr.2024.09.119