Online unsupervised detection of structural changes using train–induced dynamic responses

• Autor(a) principal: Meixedo, Andreia
• Data de Publicação: 2022
• Outros Autores: Santos, João, Ribeiro, Diogo, Calçada, Rui, Todd, Michael D.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/21729
• topic: Online assessment; Unsupervised learning; Damage detection; Structural health monitoring; Traffic-induced dynamic responses; ARX model; PCA; Cluster analysis

This paper exploits unsupervised data-driven structural health monitoring (SHM) in order to propose a continuous online procedure for damage detection based on train-induced dynamic bridge responses, taking advantage of the large-magnitude loading for enhancing sensitivity to small-scale structural changes. While such large responses induced by trains might create more damage-sensitive information in the measured response, it also amplifies the effects on those measurements from the environment. Thus, one of the biggest contributions herein is a methodology that exploits the large bridge responses induced by train passage while rejecting the confounding influences of the environment in such a way that false positive detections are mitigated. Furthermore, this research work introduces an adaptable confidence decision threshold that further improves damage detection over time. To ensure an online continuous assessment, a hybrid combination of autoregressive exogenous input (ARX) models, principal components analysis (PCA), and clustering algorithms was sequentially applied to the monitoring data, in a moving window process. A comparison between the performance obtained from autoregressive (AR) and ARX models as feature extractors was conducted, and it was concluded that ARX models lead to increased sensitivity to damage due to their ability to capture cross information between the sensors. The PCA proved its importance and effectiveness in removing observable changes induced by variations in train speed or temperature without the need to measure them, and the clustering methods allowed for an automatic classification of the damage-sensitive features. Since it was not possible to introduce damage to the bridge, several structural conditions were simulated with a highly reliable digital twin of the Sado Bridge, tuned with experimental data acquired from a SHM system installed on site, in order to test and validate the efficiency of the proposed procedure. The strategy proved to be robust when detecting a comprehensive set of damage scenarios with a false detection incidence of 2%. Moreover, it showed sensitivity to smaller damage levels (earlier in life), even when it consists of small stiffness reductions that do not impair structural safety and are imperceptible in the original signals.