Vibration-based damage detection techniques used for health monitoring of structures: a review

Das, Swagato; Saha, Purnachandra; Patro, Sanjaya Kumar

doi:10.1007/s13349-016-0168-5

Cited by 305 publications

(135 citation statements)

References 63 publications

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“…One can mention as disadvantages the fact that the technique may not be practicable if the structure has restricted accessibility or if the traffic is excessively disturbed, its application is time-discrete and the conclusion of the visual inspection is inevitably subjective. In this sense, effort was placed in developing damage detection techniques that handle measurement data to find structural changes [10].…”

Section: Background In Shmmentioning

confidence: 99%

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Neves

González

Leander

et al. 2017

J Civil Struct Health Monit

161

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As civil engineering structures are growing in dimension and longevity, there is an associated increase in concern regarding the maintenance of such structures. Bridges, in particular, are critical links in today's transportation networks and hence fundamental for the development of society. In this context, the demand for novel damage detection techniques and reliable structural health monitoring systems is currently high. This paper presents a model-free damage detection approach based on machine learning techniques. The method is applied to data on the structural condition of a fictitious railway bridge gathered in a numerical experiment using a three-dimensional finite element model. Data are collected from the dynamic response of the structure, which is simulated in the course of the passage of a train, considering the bridge in healthy and two different damaged scenarios. In the first stage of the proposed method, artificial neural networks are trained with an unsupervised learning approach with input data composed of accelerations gathered on the healthy bridge. Based on the acceleration values at previous instants in time, the networks are able to predict future accelerations. In the second stage, the prediction errors of each network are statistically characterized by a Gaussian process that supports the choice of a damage detection threshold. Subsequent to this, by comparing damage indices with said threshold, it is possible to discriminate between different structural conditions, namely between healthy and damaged. From here and for each damage case scenario, receiver operating characteristic curves that illustrate the trade-off between true and false positives can be obtained. Lastly, based on the Bayes' Theorem, a simplified method for the calculation of the expected total cost of the proposed strategy, as a function of the chosen threshold, is suggested.

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Section: Background In Shmmentioning

confidence: 99%

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Neves

González

Leander

et al. 2017

J Civil Struct Health Monit

161

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“…In the vibration-based detection methods, the damage can be detected through modifications in the linear response (modal parameters) or by the occurrence of nonlinear effects [2][3][4]. A detailed review of the previous researches on the vibration-based detection methods are given in [5][6][7]. Cao et al [8] has shown that the damping is the least applied dynamic parameters for the damage identification, because of the difficulty involved in traditional measurement.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection through the Change in the Normalized Frequency Shape

Dahak¹,

Touat²,

Benkedjouh³

2018

Vibration

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Abstract:The objective of this work is to use natural frequencies for the localization and quantification of cracks in beams. First, to study the effect of the crack on natural frequencies, a finite element model of Euler-Bernoulli is presented. Concerning the damaged element, the stiffness matrix is calculated by the theory of fracture mechanics, by inverting the flexibility matrix. Then, in order to detect damage, we are going to show that the shape given by the change in the natural frequencies is as function of the damage position only. Thus, the crack is located by the correlation between the shape of the measured frequencies and those obtained by the finite elements, where the position that gives the calculated shape which is the most similar to the measured one, indicates the crack position. After the localization, an inverse method will be applied to quantify the damage. Finally, an experimental application is presented to show the real applicability of the method, in which the crack is introduced by using an Electrical Discharge Machining. The results confirm the applicability of the method for the localization and the quantification of cracks.

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“…The field of structural health monitoring (SHM) has developed to address these challenges associated with aging infrastructure [1][2][3][4][5][6][7][8][9]. Rytter outlines criteria for classification of SHM algorithms into four levels based on their output information [2].…”

Section: Introductionmentioning

confidence: 99%

Framework for Flexural Rigidity Estimation in Euler-Bernoulli Beams Using Deformation Influence Lines

Zeinali

Story

2017

Infrastructures

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Structural degradation is an inevitable part of a structure's service life. Detecting structural impairments and assessing their nature is a significant challenge. Degradations reduce structural system stiffness and subsequently affect system deformations. An appropriate structural health index that is able to capture these changes in deformation and relate them to a structural system stiffness may help engineers to adequately rate structural condition. This paper outlines a theoretical framework for the utilization of static deformation influence lines for estimating the flexural rigidity of Euler-Bernoulli beams. In the proposed technique, the relationship between the second derivative of the deformation influence line and the flexural rigidity for both statically determinate and indeterminate beam structures is presented. The proposed method provides a flexural rigidity estimate (FRE) over the entire span that is based on a single measurement location and estimates both the location and severity of impairments, regardless of the location of the measurement or the damaged zones. Noisy analytical simulations are presented with noise levels of 0%, 0.5%, 1%, 2%, 3%, and 4%; in all cases the modeled damage is quantified and localized using the FRE. A laboratory experiment is also presented that validates the theoretical framework.

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Vibration-based damage detection techniques used for health monitoring of structures: a review

Cited by 305 publications

References 63 publications

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Crack Detection through the Change in the Normalized Frequency Shape

Framework for Flexural Rigidity Estimation in Euler-Bernoulli Beams Using Deformation Influence Lines

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