Structural damage detection by fuzzy clustering

Silva, Samuel da; Júnior, Milton Dias; Lopes, Vicente; Brennan, M.J.

doi:10.1016/j.ymssp.2008.01.004

Cited by 92 publications

(34 citation statements)

References 13 publications

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“…This ability allows classifying clustering methods as unsupervised, or baseline-free, in opposition to supervised or baseline strategies, which are based on a previous definition of data references, in which the structural condition is assumed to be known and to remain unchanged. Baseline-free clusterbased discrimination has been sucessfully applied in previous works addressing the structural assessment of civil structural and infrastructural systems (Cury & Crémona, 2012;Jung & Koh, 2009;Silva, Dias Júnior, Lopes Junior, & Brennan, 2008).…”

Section: Structure and Infrastructure Engineering 153mentioning

confidence: 99%

Baseline-free real-time assessment of structural changes

Santos

Orcesi²,

Crémona³

et al. 2014

Structure and Infrastructure Engineering

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This article addresses the subject of data-driven structural health monitoring and proposes a real-time strategy to conduct structural assessment without the need to define a baseline period, in which the monitored structure is assumed healthy and unchanged. Independence from baseline references is achieved using unsupervised discrimination machine-learning methods, widely known as clustering algorithms, which are able to find groups in data relying only on their intrinsic features and without requiring prior knowledge as input. Real-time capability is based on the definition of symbolic data, which allows describing large amounts of information without loss of generality or structural-related information. The efficiency of the proposed methodology is illustrated using an experimental case study in which structural changes were imposed to a suspended bridge during an extensive rehabilitation programme. A single-value novelty index capable of describing multisensor data is proposed, and its effectiveness in identifying structural changes in real time, using outlier analysis, is discussed.

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Section: Structure and Infrastructure Engineering 153mentioning

confidence: 99%

Baseline-free real-time assessment of structural changes

Santos

Orcesi²,

Crémona³

et al. 2014

Structure and Infrastructure Engineering

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“…In general, these damage detection approaches can be classified into the following three groups: 1) Approaches based on frequency domain data [6,7]; 2) Approaches based on time domain data [ 8,9,10]; 3) Approaches based on time-frequency domain data [11,12,13,14,15]. Methods based on other theories have also been developed, such as neural networks [16], fuzzy clustering [17], sensitivity analysis of dynamic responses [18], generalized minimum rank perturbation theory [19], proper orthogonal decomposition [20], empirical mode decomposition [21] and the information fusion technique [22].…”

Section: Introductionmentioning

confidence: 99%

Locate damage based on change in structural shape for civil space structures

Yan

Zhao

et al. 2016

SPIE Proceedings

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Recent years have witnessed a number of collapses of civil space structures, which have severely jeopardized the safety of the general public. Therefore, it is imperative to propose an approach that can localize damage to exact members at an early stage for space structures. Then, the obtained damage location results can be used by the maintenance/repair crew for taking timely actions. For most space structures, the member configuration possesses a regular pattern. For this type of structure, before damage occurs, the regular pattern in the member configuration is maintained. After damage occurs, the regular pattern around the damaged member will be destroyed. In this study, the proposed approach to locate damage is based on the change in the status of regularity of member configuration. Herein the difference in an angle of the triangular shape in the related region of a space structure is used to indicate whether the regularity of member configuration is maintained. To validate the proposed approach, a reticulated shell space structure will be investigated.

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“…Silva et al . used PCA to compress the time‐domain data before developing the autoregressive moving average model and then applied fuzzy clustering techniques to classify the damage in a benchmark frame structure . Also, Trendafilove showed that a modified PCA reduces the dimensionality of frequency response function, and that a simple pattern recognition technique can detect damage in a scaled aircraft wing model .…”

Section: Introductionmentioning

confidence: 99%

“…Ha and Golinval investigated the sensitivity of PCA to a frequency response function matrix of a beam-like model, suggesting that damage can be identified through the sensitivity variations of the principal components between the reference and the damaged state [7]. Silva et al used PCA to compress the time-domain data before developing the autoregressive moving average model and then applied fuzzy clustering techniques to classify the damage in a benchmark frame structure [8]. Also, Trendafilove showed that a modified PCA reduces the dimensionality of frequency response function, and that a simple pattern recognition technique can detect damage in a scaled aircraft wing model [9].…”

Section: Introductionmentioning

confidence: 99%

Isomap-based damage classification of cantilevered beam using modal frequency changes

Jeong

Choi

Koh

2013

Struct. Control Health Monit.

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SUMMARY This study adopts the Isomap algorithm that uses the variation of modal frequencies caused by stiffness damage to classify damage locations in a structure. The Isomap algorithm belongs to a nonlinear generalization of classical multidimensional scaling, which makes a new coordinate system for high‐dimensional data. And this coordinate system provides good observations for the extraction of the data pattern and for the classification of their nonlinear characteristics. Thus, the Isomap can easily find globally meaningful coordinates and identify the nonlinear structure of complex data sets for many cases, whereas neither the principal component analysis (PCA) nor multidimensional scaling has been successful. This study compares the performances of the PCA and the Isomap algorithm in classifying damage locations in terms of modal frequency changes. The article also investigates the benefits of incorporating the concept of sensitivity enhancing control in the Isomap‐based damage classification to improve the level of resolution of a modal frequency shift. It is demonstrated that the Isomap algorithm successfully enhances the quality of patterns and classifications related to damage locations through simulation examples. Copyright © 2013 John Wiley & Sons, Ltd.

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Structural damage detection by fuzzy clustering

Cited by 92 publications

References 13 publications

Baseline-free real-time assessment of structural changes

Baseline-free real-time assessment of structural changes

Locate damage based on change in structural shape for civil space structures

Isomap-based damage classification of cantilevered beam using modal frequency changes

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