Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

Gharehbaghi, Vahidreza; Nguyễn, Andy; Farsangi, Ehsan Noroozinejad; Yang, T.Y.

doi:10.1016/j.jobe.2020.101292

Cited by 23 publications

(10 citation statements)

References 52 publications

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“…The same approach of noise suppression was used by employing Gaussian Kernel on FRFs obtained from beam structure. 20 Recently, Gharehbaghi et al 21 used digital filters such as Finite-duration impulse response (FIR) and Infinite-duration impulse response (IIR) filters to eliminate noise from the recorded acceleration time histories. These filters can successfully eliminate noise from the specific portion of the response signal which is more contaminated with noise.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These filters can successfully eliminate noise from the specific portion of the response signal which is more contaminated with noise. 21 In another study, the curvature mode shapes were improved by using a synergy of wavelet transform (WT) and Teager energy operator (TEO). 22 From the simulated analysis, the proposed approach was able to detect three cracks with severity of 20%, 25%, and 30% in a steel beam.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Detection and localization of multiple small damages in beam

Nayyar

Baneen

Naqvi

et al. 2021

Advances in Mechanical Engineering

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Localizing small damages often requires sensors be mounted in the proximity of damage to obtain high Signal-to-Noise Ratio in system frequency response to input excitation. The proximity requirement limits the applicability of existing schemes for low-severity damage detection as an estimate of damage location may not be known a priori. In this work it is shown that spatial locality is not a fundamental impediment; multiple small damages can still be detected with high accuracy provided that the frequency range beyond the first five natural frequencies is utilized in the Frequency response functions (FRF) curvature method. The proposed method presented in this paper applies sensitivity analysis to systematically unearth frequency ranges capable of elevating damage index peak at correct damage locations. It is a baseline-free method that employs a smoothing polynomial to emulate reference curvatures for the undamaged structure. Numerical simulation of steel-beam shows that small multiple damages of severity as low as 5% can be reliably detected by including frequency range covering 5–10th natural frequencies. The efficacy of the scheme is also experimentally validated for the same beam. It is also found that a simple noise filtration scheme such as a Gaussian moving average filter can adequately remove false peaks from the damage index profile.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Detection and localization of multiple small damages in beam

Nayyar

Baneen

Naqvi

et al. 2021

Advances in Mechanical Engineering

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“…Since this method is based on a partial structural dynamics model, tit can identify even a small number of vibrations [15]. In this area, autoregressive (AR) models are investigated for damage and deterioration detection in buildings and bridges [16][17][18]. Autoregressive and moving average model (ARMA), as well as generalized autoregressive conditional heteroscedasticity model (GARCH), have proved to be beneficial for nonlinear damage identification in building specimens [19].…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model

Gharehbaghi

Kalbkhani

Farsangi

et al. 2022

Engineering with Computers

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In this article, an original data-driven approach is proposed to detect both linear and nonlinear damages in structures using output-only responses. The method deploys variational mode decomposition (VMD) and generalized autoregressive conditional heteroscedasticity (GARCH) model for signal processing and feature extraction. To this end, VMD decomposes the responsesignals are first decomposed to intrinsic mode functions (IMFs), and then, GARCH model is utilized to represent the statistics of IMFs. The model coefficients' of IMFs construct the primary feature vector. Kernel-based principal component analysis (PCA) and linear discriminant analysis (LDA) are utilized to reduce the redundancy from the primary features by mapping them to the new feature space. The informative features are then fed separately into three supervised classifiers: support vector machine (SVM), k-nearest neighbor (kNN), and fine tree. The performance of the proposed method is evaluated on two experimental scaled models in terms of linear and nonlinear damage assessment. Kurtosis and ARCH tests proved the compatibility of GARCH model. The results demonstrate that the proposed technique reaches the accuracy of 100% and 98.82% in classifying linear and nonlinear damages, respectively. Also, its accuracy is higher than 80% in the presence of noise with a signal-to-noise ratio (SNR) higher than 10 dB.

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“…Further, Ebrahimian et al and Hariri-Ardebili et al [57][58][59][60][61][62] investigated the damage parameter identification in the framework of structural health monitoring by using an extended version of Kalman filter. Damage detection for the purpose of health monitoring is also done by Yan et al [63] by using the Kalman filter and other stochastic approaches by Kourehli et al and Gharehbaghi et al [64][65][66].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Adeli

Rosić

Matthies

et al. 2020

Metals

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The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

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Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

Cited by 23 publications

References 52 publications

Detection and localization of multiple small damages in beam

Detection and localization of multiple small damages in beam

A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

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