Structural damage detection using Bayesian inference and seismic interferometry

Uzun, Murat; Sun, Hao; Smit, Dirk; Büyüköztürk, Oral

doi:10.1002/stc.2445

Cited by 16 publications

(10 citation statements)

References 61 publications

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“…In the stretching method, on the other hand, a time delay of s k [ t ] is modeled by stretching the time axis of s 0 [ t ], schematically illustrated in Figure 1. First, s 0 [ t ] is interpolated at times t (1+ α ) with various values of α for the signal stretching in the time window of [ t 1 , t 2 ] 31,32 . Then, the stretching factor ( α k ) is obtained by selecting a α value that maximizes the CC value between s k [ t ] and s 0 [ t (1+ α )] as follows:

{CC}_{k} ((), α) = \frac{\int_{t_{1}}^{t_{2}} s_{0} ([], t ((), 1 + α)) s_{k} ([], t) italicdt}{\sqrt{\int_{t_{1}}^{t_{2}} s_{0}^{2} [t (1 + α)] dt \int_{t_{1}}^{t_{2}} s_{k}^{2} [t] dt}},

and

α_{k} = \underset{α}{argmax} ((), {CC}_{k} ((), α))

Similar to the doublet method, the time lag ( α k ) or the decorrelation coefficient (1 − CC k ( α k )) values can be obtained and used for damage assessment 16,33 .…”

Section: Coda Wave Interferometry (Cwi)mentioning

confidence: 99%

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Fatigue damage detection and growth monitoring for composite structure using coda wave interferometry

Lim

Lee

Skinner

et al. 2020

Struct Control Health Monit

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Summary This paper presents the development of a fatigue damage detection and growth monitoring technique based on coda wave interferometry (CWI) for composite structures. The time domain distortion (TD) component of coda waves, which is typically ignored as noise in the literature, is used and experimentally validated. First, a widely used signal processing method for CWI called the stretching method is applied, and TD component is extracted in the time domain. Next, threshold values are statistically estimated using noise levels of the signal and compared with the amplitude of TD component for detecting early‐stage fatigue damage. A damage index (DI) is then obtained from TD component and used to monitor fatigue damage growth. The experimental results of the damage detection and growth monitoring are validated through visual inspection during the fatigue test. Finally, the developed technique's performance is evaluated by comparing it to conventional ultrasonic and CWI‐based composite fatigue damage assessment methods. The uniqueness of this study lies in (1) detection and growth monitoring of the early‐stage composite fatigue damage, (2) extraction and use of TD component of CWI and investigation of the correlation with the damage existence and growth, and (3) experimental validation and performance examination by comparing with the conventional ultrasonic and CWI‐based composite fatigue damage assessment methods.

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{CC}_{k} ((), α) = \frac{\int_{t_{1}}^{t_{2}} s_{0} ([], t ((), 1 + α)) s_{k} ([], t) italicdt}{\sqrt{\int_{t_{1}}^{t_{2}} s_{0}^{2} [t (1 + α)] dt \int_{t_{1}}^{t_{2}} s_{k}^{2} [t] dt}},

and

α_{k} = \underset{α}{argmax} ((), {CC}_{k} ((), α))

Similar to the doublet method, the time lag ( α k ) or the decorrelation coefficient (1 − CC k ( α k )) values can be obtained and used for damage assessment 16,33 .…”

Section: Coda Wave Interferometry (Cwi)mentioning

confidence: 99%

“…Finally, in the stretching method, s k [ t ] can be expressed as 23,32

s_{k} ([], t) = s_{0} ([], t ((), 1 + α_{k})) + d_{k} ([], t)

where d k [ t ] is TD component representing the additional slight signal variation after the signal stretching (time delay) in the time domain, which has been ignored as noise or weak decorrelation in previous researches.…”

Section: Coda Wave Interferometry (Cwi)mentioning

confidence: 99%

Fatigue damage detection and growth monitoring for composite structure using coda wave interferometry

Lim

Lee

Skinner

et al. 2020

Struct Control Health Monit

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“…Later, Chatzielefttheriou and Lagaros 5 proposed a new two-loop trajectory method to conduct damage identification. Furthermore, the spectral density method 6 , the Bayesian method [7][8][9][10][11][12] , and the Kalman filter 13 have been, respectively, employed in the vibration-based structural identification.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding

Hao

2020

Struct Control Health Monit

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Summary The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan‐based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

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“…The advantage of the Bayesian formulation is that the inversion tends to be more robust and associated uncertainties can be naturally quantified through statistical properties. In general, the posterior is obtained by Monte Carlo (MC)-based sampling techniques such as importance sampling and Markov chain Monte Carlo (MCMC) methods [12][13][14], where a large number of samples are required and thus the computation becomes intractable for any nontrivial forward model (e.g., CFD simulations of complex flows).…”

Section: Introductionmentioning

confidence: 99%

A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems

Gao,

Wang

2020

Preprint

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Mathematical modeling and simulation of complex physical systems based on partial differential equations (PDEs) have been widely used in engineering and industrial applications.To enable reliable predictions, it is crucial yet challenging to calibrate the model by inferring unknown parameters/fields (e.g., boundary conditions, mechanical properties, and operating parameters) from sparse and noisy measurements, which is known as a PDE-constrained inverse problem. In this work, we develop a novel bi-fidelity (BF) ensemble Kalman inversion method to tackle this challenge, leveraging the accuracy of high-fidelity models and the efficiency of low-fidelity models. The core concept is to build a BF model with a limited number of high-fidelity samples for efficient forward propagations in the iterative ensemble Kalman inversion. Compared to existing inversion techniques, salient features of the proposed methods can be summarized as follow: (1) achieving the accuracy of high-fidelity models but at the cost of low-fidelity models, (2) being robust and derivative-free, and (3) being code nonintrusive, enabling ease of deployment for different applications. The proposed method has been assessed by three inverse problems that are relevant to fluid dynamics, including both parameter estimation and field inversion. The numerical results demonstrate the excellent performance of the proposed BF ensemble Kalman inversion approach, which drastically outperforms the standard Kalman inversion in terms of efficiency and accuracy.

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Structural damage detection using Bayesian inference and seismic interferometry

Cited by 16 publications

References 61 publications

Fatigue damage detection and growth monitoring for composite structure using coda wave interferometry

Fatigue damage detection and growth monitoring for composite structure using coda wave interferometry

Structural damage identification by sparse deep belief network using uncertain and limited data

A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems

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