Structural health monitoring strategy for damage detection in railway bridges using traffic induced dynamic responses

• Type: Chapter or Part of a Book
• Date: 2022

Authors

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

Book

Title: Rail Infrastructure Resilience: A Best-Practices Handbook

Pages: 389-408

ISBN: 978-012821042-0, 978-012821043-7

Indexing

Scopus - 5 Citations

Other information

Authenticus ID: P-00X-6GK

DOI: 10.1016/b978-0-12-821042-0.00011-3

Abstract (EN): This book chapter aims at detecting damage in railway bridges based on traffic-induced dynamic responses. To achieve this objective, an unsupervised automatic data-driven SHM strategy is presented, consisting of a hybrid combination of multivariate statistical techniques. Damage-sensitive features of train-induced responses are extracted and allow taking advantage, not only of the repeatability of the loading, but also, and more relevant, of its great magnitude, thus enhancing the sensitivity to small-magnitude structural changes. The efficiency of the proposed strategy is validated in a long-span steel-concrete composite bowstring-arch railway bridge with a continuous structural monitoring system installed. An experimentally validated finite element model was used, along with experimental values of noise, temperature, and train loadings and speeds, to simulate undamaged and damaged scenarios. The strategy proved to be highly sensitive in detecting a novelty, even when it consists of small stiffness reductions that do not impair the safety or use of the structure, and are highly robust to false detections. The analysis and validation allowed concluding that the ability to identify a novelty imperceptible in the original signals, while avoiding observable changes induced by variations in temperature or train speed, was accomplished by wisely defining the modeling and fusion sequence of the information. A single-value damage indicator, proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition, proved capable of characterizing multisensor data while being sensitive to identify local changes. © 2022 Elsevier Inc. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific