Automatic wavelet-based clustering approach for damage detection on railway bridges

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Meixedo, A (Author) Other; Ribeiro, D (Author) Other; Santos, J (Author) Other; Rui Calçada (Author) FEUP; Todd, M (Author) Other

Conference proceedings International

Title: Transportation Research Procedia

Pages: 4287-4294

2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022

Lisboa, 14 November 2022 through 17 November 2022

Indexing

Scopus

Other information

Authenticus ID: P-00Z-VVM

DOI: 10.1016/j.trpro.2023.11.629

Abstract (EN): This paper addrebes unsupervised damage detection in railway bridges by presenting a novel AI-based SHM methodology using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelets, PCA and cluster analysis is implemented. Damage-sensitive features from train-induced dynamic responses are extracted and allow taking advantage not only of the repeatability of the loading, but also, of its large magnitude, thus enhancing sensitivity to small-magnitude structural changes. The effectiveness of the proposed methodology is validated in a long-span bowstring-arch railway bridge with a permanent structural monitoring system installed. The methodology proved highly sensitive in detecting early damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. © 2023 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)

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7