One-year operational modal analysis of a steel bridge from high-resolution macrostrain monitoring: Influence of temperature vs. retrofitting

Anastasopoulos, D.; Roeck, Guido De; Reynders, Edwin

doi:10.1016/j.ymssp.2021.107951

Cited by 50 publications

(25 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, a second independent vibration monitoring system consisting of 80 fiber Bragg grating (FBG) strain measurements is present on the railway bridge [17]. The monitoring covers a shorter time period.…”

Section: Measurement Setupmentioning

confidence: 99%

Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51

Maes

Meerbeeck

Reynders

et al. 2022

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

Section: Measurement Setupmentioning

confidence: 99%

Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51

Maes

Meerbeeck

Reynders

et al. 2022

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

“…Zeng and Hoon Kim 20 tested a self‐adaptive clustering approach with a weighted multi‐term distance on the Z24 and Downing Hall benchmarks. The classic hierarchical clustering‐based AOMA was applied by Anastasopoulos et al 21 to almost one year of acquisitions from a steel single‐span tied arch railway bridge. Indeed, the Structural Mechanics Section of KU Leuven developed a MatLab® Toolbox, 22 which, similarly to the algorithm applied here for this study, includes (among some other options) an SSI algorithm 23 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Civera

Mugnaini

Fragonara

2022

Structural Contr & Hlth

View full text Add to dashboard Cite

Structural health monitoring (SHM) is one of the main research topics in civil, mechanical and aerospace engineering. In this regard, modal parameters and their trends over time can be used as features and indicators of damage occurrence and growth. However, for practical reasons, output-only techniques are particularly suitable for the system identification (SI) of large civil structures and infrastructures, as they do not require a controlled source of input force.In this context, these approaches are typically referred to as operational modal analysis (OMA) techniques. However, the interpretation of the OMA identifications is a labour-intensive task, which could be better automated with artificial intelligence and machine learning (ML) techniques. In particular, clustering and cluster analysis can be used to group unlabelled datasets and interpret them. In this study, a novel multi-stage clustering algorithm for automatic OMA (AOMA) is tested and validated for SHM applications-specifically, for damage detection and severity assessment-to a masonry arch bridge. The experimental case study involves a 1:2 scaled model, progressively damaged to simulate foundation scouring at the central pier.

show abstract

“…In this paper, we consider part of a railway bridge. SHM for such a structure is usually based on vibration or strain measurements where anomalies are detected by spotting variations in engineered features such as the modal parameters [2]. This work aims to build an anomaly detector that is not based on engineered features, but rather on signal patterns directly found in the strain gauge measurements.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection and representation learning in an instrumented railway bridge

Bel-Hadj¹,

Weijtjens²,

Santos³

2022

ESANN 2022 Proceedings

View full text Add to dashboard Cite

In this contribution, the strain measurements of a railway bridge are used for anomaly detection, in the context of Structural Health Monitoring (SHM). The methodology used is a combination of a sparse convolutional autoencoder (CSAE) and a Mahalanobis distance. Due to the lack of labeled anomalous data, a simulated fault is used to evaluate the performance of the algorithm. The proposed approach far outperforms the classical feature-based approach. Finally, the latent dimension of the autoencoder is studied and shown to be structured and representative of the underlying physics of the problem.

show abstract

One-year operational modal analysis of a steel bridge from high-resolution macrostrain monitoring: Influence of temperature vs. retrofitting

Cited by 50 publications

References 43 publications

Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51

Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51

Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Anomaly detection and representation learning in an instrumented railway bridge

Contact Info

Product

Resources

About