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

Maes, Kristof; Meerbeeck, Lize Van; Reynders, Edwin; Lombaert, Geert

doi:10.1016/j.ymssp.2021.108380

Cited by 71 publications

(26 citation statements)

References 23 publications

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“…In particular, for this case, we investigate if the effect of a sudden application of the concentrated load P can be observed from the data set. The description of both methods in the following subsections is borrowed from [31] and is repeated here for the sake of completeness.…”

Section: Anomaly Detectionmentioning

confidence: 99%

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Anomaly detection in long-term tunnel deformation monitoring

Maes

Salens²,

Feremans³

et al. 2022

Engineering Structures

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Section: Anomaly Detectionmentioning

confidence: 99%

“…Ψ = U 1 . The number of principal components n c accounted for in the PCA is generally determined from the model misfit in the training and the validation stages, as outlined in [31]. In the present numerical study, only the first three (out of 18) principal components have been retained (n c = 3).…”

Section: Linear Pcamentioning

confidence: 99%

Anomaly detection in long-term tunnel deformation monitoring

Maes

Salens²,

Feremans³

et al. 2022

Engineering Structures

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“…The retrofit was completed on 27th September 2019. Novelty detection has been successfully performed on this dataset using robust principal component analysis (PCA) and linear regression methods [33]. For a complete overview of the dataset the reader is referred to [20].…”

Section: Z24 and Kw51 Datasetsmentioning

confidence: 99%

Domain-adapted Gaussian mixture models for population-based structural health monitoring

Gardner

Bull

Dervilis

et al. 2022

J Civil Struct Health Monit

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Transfer learning, in the form of domain adaptation, seeks to overcome challenges associated with a lack of available health-state data for a structure, which severely limits the effectiveness of conventional machine learning approaches to structural health monitoring (SHM). These technologies utilise labelled information across a population of structures (and physics-based models), such that inferences are improved, either for the complete population, or for particular target structures — enabling a population-based view of SHM. The aim of these methods is to infer a mapping between each member of the population’s feature space (called a domain) in which a classifier trained on one member of the population will generalise to the remaining structures. This paper introduces the domain-adapted Gaussian mixture model (DA-GMM) for population-based SHM (PBSHM) scenarios. The DA-GMM, infers a linear mapping that transforms target data from one structure onto a Gaussian mixture model that has been inferred from source data (from another structure). The proposed model is solved via an expectation maximisation technique. The method is demonstrated on three case studies: an artificial dataset demonstrating the approach’s effectiveness when the target domain differs by two-dimensional rotations; a population of two numerical shear-building structures; and a heterogeneous population of two bridges, the Z24 and KW51 bridges. In each case study, the method is shown to provide informative results, outperforming other conventional forms of GMM (where no target labelled data are assumed available), and provide mappings that allow the effective exchange of labelled information from source to target datasets.

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“…During the monitoring campaign, each diagonal member was retrofitted with a steel box to strengthen the design of the bridge, with the retrofit beginning on the 15 th of May 2019 and completed on the 27 th of September 2019. Novelty detection of the retrofit has been successfully demonstrated using robust PCA and linear regression trained on the pre-retrofit data [70]. For a full description of the dataset, the reader is referred to [43].…”

Section: Datasetsmentioning

confidence: 99%

On statistic alignment for domain adaptation in structural health monitoring

Poole¹,

Gardner²,

Dervilis³

et al. 2022

Preprint

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The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning -specifically in the form of domain adaptation (DA) -gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Typical DA methods rely on nonparametric distance metrics, which require sufficient data to perform density estimation. In addition, these methods can be prone to performance degradation under class imbalance. To address these issues, statistic alignment (SA) is discussed, with a demonstration of how these methods can be made robust to class imbalance, including a special case of class imbalance called a partial DA scenario. SA is demonstrated to facilitate damage localisation with no target labels in a numerical case study, outperforming other state-of-the-art DA methods. It is then shown to be capable of aligning the feature spaces of a real heterogeneous population, the Z24 and KW51 bridges, with only 220 samples used from the KW51 bridge. Finally, in scenarios where more complex mappings are required for knowledge transfer, SA is shown to be a vital pre-processing tool, increasing the performance of established DA methods.

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

Cited by 71 publications

References 23 publications

Anomaly detection in long-term tunnel deformation monitoring

Anomaly detection in long-term tunnel deformation monitoring

Domain-adapted Gaussian mixture models for population-based structural health monitoring

On statistic alignment for domain adaptation in structural health monitoring

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