Damage detection using the eigenparameter decomposition of substructural flexibility matrix

Weng, Shun; Zhu, Hong; Xia, Yong; Mao, Ling

doi:10.1016/j.ymssp.2012.08.001

Cited by 56 publications

(48 citation statements)

References 21 publications

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“…Although easier to extract from dynamic measurements, the flexibility matrix may not be linked to damage quantification and localization in a manner that is as straightforward as the stiffness. Similar approaches are further explored in the works of Nobahari and Seyedpoor and Weng et al…”

Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 94%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

216

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 94%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

216

112

View full text Add to dashboard Cite

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“…However, a scalar indicator for each structural element is needed. One approach used by many researchers to describe a matrix that contains damage information is eigenvalue decomposition [11,28,34,38,39]. Applying this approach to each matrix [x s ] i we obtain…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

“…It is well known that noise in modal measurements and errors due to modal truncation are the main variables that affect structural damage detection methods based on flexibility matrices [27][28][29][30]. For this reason, we evaluated the sensitivity of DFM when these variables were considered.…”

Section: Effects Of Limited Modal Information and Noisementioning

confidence: 99%

“…Li et al [27] developed a damage identification method using a "generalized flexibility matrix," by which they used to detect changes in stiffness in a simply supported beam using frequencies and modal shapes of the damaged and undamaged structure. Weng et al [28] proposed a new substructuring method for damage detection in a structure; they obtained substructural flexibility matrices corresponding to each substructure and calculated the corresponding eigenvalues and eigenvectors to use them as indicators of damage. is method is very sensitive to localized damage; however, it is necessary to have many accelerometers close to the damage location.…”

Section: Introductionmentioning

confidence: 99%

“…Many damage detection methods based on structural flexibility have been developed. Despite their ability to estimate the flexibility matrix of a structure from measured lower-order modal shapes, these methods still suffer from modal truncation error, particularly on a complex structure with many higher modes that are not measured [27][28][29][30]. In this paper, we present the damaged flexibility matrix method (DFM), a new iterative damage detection method using the procedure developed by Baruch [31], to optimize and correct flexibility matrices from vibration measurements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

Oyola¹,

Fonseca²,

Rocha³

2020

Advances in Civil Engineering

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is study presents the damaged flexibility matrix method (DFM) to identify and determine the magnitude of damage in structural elements of plane frame buildings. Damage is expressed as the increment in flexibility along the damaged structural element. is method uses a new approach to assemble the flexibility matrix of the structure through an iterative process, and it adjusts the eigenvalues of the damaged flexibility matrices of each system element. e DFM was calibrated using numerical models of plane frames of buildings studied by other authors. e advantage of the DFM, with respect to other flexibility-based methods, is that DFM minimizes the adverse effect of modal truncation. e DFM demonstrated excellent accuracy with complete modal information, even when it was applied to a more realistic scenario, considering frequencies and modal shapes measured from the recorded accelerations of buildings stories. e DFM also presents a new approach to simulate the effects of noise by perturbing matrices of flexibilities. is approach can be useful for research on realistic damage detection. e combined effects of incomplete modal information and noise were studied in a ten-story four-bay building model taken from the literature. e ability of the DFM to assess structural damage was corroborated. Application of the proposed method to a ten-story four-bay building model demonstrates its efficiency to identify the flexibility increment in damaged structural elements.

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On-line unsupervised detection of early damage

Santos

Crémona

Calado

et al. 2015

Struct. Control Health Monit.

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Structural health monitoring (SHM) strategies should ideally consist of continuous on-line damage detection processes, which do not need to rely on the comparison of newly acquired data with baseline references, previously defined assuming that structural systems are undamaged and unchanged during a given period of time.The present paper addresses the topic of SHM and describes an original strategy for detecting damage in an early stage without relying on the definition of data references. This strategy resorts to the combination of two statistical learning methods. Neural networks were used to estimate the structural response, and clustering methods were adopted for automatically classifying the neural networks' estimation errors. To ensure an on-line continuous process, these methods were sequentially applied in a moving windows process.The proposed original strategy was tested and validated on numerical and experimental data obtained from a cable-stayed bridge. It proved highly robust to false detections and sensitive to early damage by detecting small stiffness reductions in single stay cables as well as the detachment of neoprene pads in anchoring devices, resorting only to a small amount of inexpensive sensors. detection approaches rely on signal processing and statistical learning techniques to extract sensitive information from time-series acquired on site [8][9][10][11]. Their computational simplicity makes them cost-effective and the most suitable candidates for carrying out automated on-line damage detection [2], either based on modal information [12][13][14] or based on statistical and time-series features [2,8,15].Data-driven SHM approaches rely on two mandatory steps for conducting damage detection: response modelling and statistical classification. The first aims at separating the variations imposed by 'normal' environmental/operational actions from those caused by damage [16]. It relies on training statistical learning algorithms so that they can accurately estimate the 'normal' structural response. Any 'abnormal' variations can afterwards be highlighted by comparing the estimates with the actual responses. The most reported statistical modelling algorithms found in SHM literature consist of multi-layer perceptron (MLP) neural networks [17][18][19], support vector regressions [20], linear regressions [2], principal component analysis [21] and auto associative neural networks [22]. Regardless of the chosen algorithms, response modelling has been reported in the literature as a supervised problem, where the statistical learning algorithms are trained a priori with reference data, in which the structural systems must be assumed undamaged and unchanged [9,11,23].Statistical classification consists of discriminating SHM data as related to identical or distinct structural conditions [24,25]. This step has also been addressed under supervised approaches, where classification algorithms are trained with reference data sets (in general, the same ones used for response modelling) to define boundaries that s...

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Damage detection using the eigenparameter decomposition of substructural flexibility matrix

Cited by 56 publications

References 21 publications

Recent progress and future trends on damage identification methods for bridge structures

Recent progress and future trends on damage identification methods for bridge structures

Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

On-line unsupervised detection of early damage

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