Monitoring Railway Bridge KW51 Before, During, and After Retrofitting

Maes, Kristof; Lombaert, Geert

doi:10.1061/(asce)be.1943-5592.0001668

Cited by 51 publications

(58 citation statements)

References 7 publications

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“…By linking structures via mappings, any labelled data obtained for one structure can be applied to the rest of the population. In this section, domain adaptation is performed, such that a mapping is obtained in a heterogeneous population of two partially-labelled 3 bridge datasets, from the Z24 [19] and KW51 bridges [20]. By learning a mapping between the two bridge datasets, any future label information obtained for one structure can be directly applied to the other.…”

Section: Case Study: Z24 and Kw51 Bridgesmentioning

confidence: 99%

“…The novelty in this paper is that the method has been extended to the scenario where the target is unlabelled (and even the scenario where the source is unlabelled as well). This extension makes the approach practical for PBSHM scenarios, with the method demonstrated on three case studies: an artificial dataset, a population of two numerical shear-building structures, and the Z24 [19] and KW51 [20] bridge datasets. A MATLAB implementation accompanies this paper-https:// github.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Case Study: Z24 and Kw51 Bridgesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gardner

Bull

Dervilis

et al. 2022

J Civil Struct Health Monit

View full text Add to dashboard Cite

show abstract

“…The result is the availability of the evolution of the modal parameters (natural frequencies, damping ratios and mode shapes) of the railway bridge over time. For a complete description about the measurement setup, data processing and the available data characteristics readers can refer to [ 36 ]. For the purposes of this paper, the four natural frequencies related to the vertical global modes of the bridge deck are considered, referred to as

and

in Figure 5 a.…”

Section: Application To Shm: the Railway Bridge Kw51mentioning

confidence: 99%

“…Alternatively, many works rely on the use of experimental data from the well-known Z24 bridge benchmark study [ 33 , 34 , 35 ], where long-term continuous monitoring took place during the year before demolition and progressive failure tests were performed. In order to increase the availability of validation cases related to different types of real structures and damages, the method proposed in this paper considers the experimental data from the monitoring campaign of the KW51 railway bridge in Leuven [ 36 ] over a period of 15 months, recently acquired by the research group from the Structural Mechanics Section of the KU Leuven University.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

Turrisi

Zappa

Cigada

2022

Sensors

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Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

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“…Data collected during railway restoration and construction work can be utilized for maintenance purposes. Dataset [76] includes monitoring data for a railway bridge before, during and after a retrofitting process. The bridge is located in KW51 in Leuven, Belgium.…”

Section: Construction Workmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

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Monitoring Railway Bridge KW51 Before, During, and After Retrofitting

Cited by 51 publications

References 7 publications

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

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

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

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