Online Identification of Linear Time-varying Stiffness of Structural Systems by Wavelet Analysis

Basu, Biswajit; Nagarajaiah, Satish; Chakraborty, Arunasis

doi:10.1177/1475921707081968

Cited by 75 publications

(40 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Han et al investigated the application of wavelet packet sifting for detecting damage to a beam structure [10]. Some robust ways to compute time-varying frequency of dynamic system using the wavelet analysis had been studied by Ghanem and Romeo [11], Goggins et al [12] and Basu et al [13]. Using nonlinear features is believed to improve the damage classification capacity of a damage detection scheme.…”

Section: Introductionmentioning

confidence: 99%

System identification and damage evaluation of degrading hysteresis of reinforced concrete frames

Loh

Mao

Huang

et al. 2010

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

In this study, signal processing approaches and nonlinear identification are used to measure seismic responses of reinforced concrete (RC) structures using the shaking table test. To analyze structural nonlinearity, an equivalent linear system with time-varying model parameters, singular spectrum analysis to elucidate residual deformation, and wavelet packet transformation analysis to yield the energy distribution among components are adopted to detect the nonlinearity. Then, damage feature extraction is conducted using both the Holder exponent and the Level-1 detail of the discrete wavelet component. Finally, the modified Bouc-Wen hysteretic model and the system identification process are employed to the shaking table test data to evaluate the physical parameters, including the stiffness degradation, the strength deterioration and the pinching hysteresis. Finally, the identified stiffness and strength degradation functions from the test data of RC frames in relation to the degree of ground shaking, damage index and the identified nonlinear features are discussed. Based on the proposed method, both signal-based and modelbased identifications, the relationship between the damage occurrence and severity of structural damage can be identified. Figure 9. (a) Comparison of Inter-story drift ratio, the Holder exponent, Level-1 detail component and residuals estimated from SSA with respect to time from response data of RCF6. Correlation of singularities is identified among different analyses; (b) comparison of Inter-story drift ratio, the Holder exponent, Level-1 detail component and residuals estimated from SSA with respect to time from response data of RCF2. Correlation of singularities is identified among different analyses; and (c) comparison of the Inter-story drift ratio, the Holder exponent, Level-1 detail component and residuals estimated from SSA with respect to time from response data of RCF4. Correlation of singularities is identified among different analyses.To quantify the damage to these four RC frames, the hysteretic model parameters were evaluated from the load-displacement data of the RC frame. The original rough data were not used; rather, the SSA method was applied to filter out the high-frequency components (>20.0 Hz) of the recorded

show abstract

Section: Introductionmentioning

confidence: 99%

System identification and damage evaluation of degrading hysteresis of reinforced concrete frames

Loh

Mao

Huang

et al. 2010

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…In this vein, numerous damage detection methods have been developed in the past decades. Among them, vibration‐based damage detection techniques, which rely on global dynamic characteristics, can provide crucial information that can complement the localized methods and visual inspections; this has prompted their use on various bridges in recent decades …”

Section: Introductionmentioning

confidence: 99%

Damage detection under varying temperature using artificial neural networks

Gül

2017

Struct Control Health Monit

View full text Add to dashboard Cite

Summary To avoid false alarms for vibration‐based structural damage detection methods, temperature effects on damage‐sensitive features should be eliminated. In this paper, a novel two‐step damage identification method combining a multilayer neural network and novelty detection is developed to differentiate the changes in natural frequencies (one of the most commonly used damage features that can be obtained reliably and relatively easily) due to damage from those induced by temperature variations. In the first step, a multilayer artificial neural network, which resembles an auto‐associative neural network but uses temperature variables in addition to the frequencies as the inputs, is explored to identify patterns in frequencies of undamaged structures under varying temperatures. Euclidean distance is then utilized as a novelty index to quantify the discordancy between patterns in undamaged cases and candidate cases. Numerical studies using a simply supported beam and finite element models based on an experimental grid structure, which simulate different levels of stiffness reductions under varying temperature conditions, are used to verify the detectability and robustness of the proposed approach. It is shown that the incorporation of the proposed artificial neural network with novelty detection enables one to robustly distinguish damage occurrence and severity regardless of temperature variations and noise perturbations. Using an unsupervised learning scheme, the proposed approach transforms a multivariate analysis using modal frequencies and temperature data into a straightforward univariate discordancy test using the novelty index. Given these competitive advantages, this approach is very attractive for the development of an automated continuous monitoring system in practical applications.

show abstract

“…Several researchers have explored this issue of detection of sudden changes in stiffness. Some of the most common methods for detecting damage, and the discontinuities either in the time or spatial domains, have been the continuous and discrete wavelet transform‐based methods 3–25 although there are also several researchers that have used the Hilbert–Huang Transform approach 26–29. Very good reviews of the application of the wavelet‐based methods in SHM is provided by 3–5, and 25.…”

Section: Introductionmentioning

confidence: 99%

“…Very good reviews of the application of the wavelet-based methods in SHM is provided by [3][4][5], and [25]. The specific applications to detect discontinuities in spatial domains such as along a beam or a plate are described in References [6][7][8][9][10][11][12][13][14][15][16][17][18], and for detecting discontinuities or track changes in time domain in References [19][20][21][22][23][24][25]. Singularities in the measured signal are typically indicated by the presence of significant non-zero value(s) or spikes in the discrete wavelet transform details [4,23].…”

Section: Introductionmentioning

confidence: 99%

Detecting sudden changes in stiffness using high-pass filters

Bisht¹,

Singh²

2011

Struct. Control Health Monit.

View full text Add to dashboard Cite

SUMMARY It is often of interest to detect the time of occurrence of sudden change in structural parameters, particularly the changes in structural stiffness for health monitoring purposes as a change in stiffness also implies some damage in one or more structural elements. Although well known, it is formally shown here that a sudden change in stiffness induces a sudden change in the acceleration response. Several methods have been used in the literature to detect such sudden changes. The wavelet transform, in particular the discrete wavelets transform, is one of the popular approaches. In this approach, the sudden changes have been shown to manifest themselves as spikes in the plots of the details of the discrete wavelet transform of the acceleration response. In this paper, we provide an analytical rationale to explain what constitutes these spikes and their relationship to the discontinuity being detected. In particular, it is shown that a spike corresponds to the scaled step‐response of a high‐pass filter and that a step discontinuity can be detected using a high‐pass filter with properly chosen cut‐off frequency where the discontinuity manifests as a clear spike in the filtered output. How the measurement error and sampling frequency affect the detection is discussed. An example of a multi‐degree‐of‐freedom system subjected to the base excitation, with sudden changes in its structural elements, is presented to illustrate the approach. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

Online Identification of Linear Time-varying Stiffness of Structural Systems by Wavelet Analysis

Cited by 75 publications

References 48 publications

System identification and damage evaluation of degrading hysteresis of reinforced concrete frames

System identification and damage evaluation of degrading hysteresis of reinforced concrete frames

Damage detection under varying temperature using artificial neural networks

Detecting sudden changes in stiffness using high-pass filters

Contact Info

Product

Resources

About