Sensitivity-based damage detection algorithm for structures using vibration data

Krishnanunni, C.G.; Raj, Rahul; Nandan, Deepak; Midhun, C. K.; Sajith, A. S.; Ameen, Mohammed

doi:10.1007/s13349-018-0317-0

Cited by 24 publications

(13 citation statements)

References 18 publications

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“…The damage is represented as a crack of depth d c that causes an exponential decrease in the flexural stiffness away from the crack location 39 . The stiffness reduction factor (SRF) is adopted as an appropriate damage indicator 40 . If δ K b represents the change in global stiffness matrix of the bridge due to the damage and δk e gives the SRF for each individual finite elements, the following relation holds:

\begin{matrix} right δ K_{b} = & left - {falsefalse}_{\sum}^{e = 1} δ k_{e} \times [K_{e}] & right \\ right {K_{b}}^{d a m a g e d} = & left {K_{b}}^{u n d a m a g e d} + δ K_{b} \end{matrix}

where m is the number of elements and [ K e ] represents the beam element stiffness matrix.…”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Krishnanunni

Rao

2021

Struct Control Health Monit

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Summary This paper presents a novel damage identification technique for bridges based on the dynamic response of a moving vehicle. A quarter car vehicle model instrumented with two accelerometers and an inertial profilometer is used for this purpose. The method involves the coupling of Tikhonov regularization scheme with signal averaging technique to handle the problem of measurement noise and road roughness. In the first stage, a damage‐dependent road roughness profile is estimated from measured acceleration response by minimizing a Tikhonov regularized least squares cost function. The second stage involves the minimization of the profile roughness residual function that depends on the location and magnitude of damage. This objective function compares the roughness profile computed from the first stage and that measured by the inertial profilometer. It is proved that efficient damage detection ensues from considering multiple runs of the vehicle and choosing the appropriate regularization parameter. The present study considers various aspects, such as the uniqueness of results, robustness of results to measurement noise, effect of modelling error, effect of vehicle speed, and uncertainty in estimation. Numerical results show that the approach is capable of detecting the magnitude as well as the location of damage. In addition, the problem of road roughness and measurement noise is handled competently.

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\begin{matrix} right δ K_{b} = & left - {falsefalse}_{\sum}^{e = 1} δ k_{e} \times [K_{e}] & right \\ right {K_{b}}^{d a m a g e d} = & left {K_{b}}^{u n d a m a g e d} + δ K_{b} \end{matrix}

where m is the number of elements and [ K e ] represents the beam element stiffness matrix.…”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Krishnanunni

Rao

2021

Struct Control Health Monit

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“…The final evaluation result of Figure 26 was very good since the true damage was correctly detected in element 9. For comparisons, the damage detection results reported by Krishnanunni et al [8] and Hao et al [38] are presented in Figure 27, obtained by Cuckoo Search algorithm (CSA) and Genetic algorithm (GA), respectively. Meanwhile, the result of Figure 26 is also shown in Figure 27 for easy comparison.…”

Section: Experimental Validationmentioning

confidence: 99%

“…Moreover, the computational complexity of the ESVT method was significantly lower compared to the other methods because both CSA and GA needed many iterations for good convergence. For example, the computation process using CSA reported by Krishnanunni et al [8] was iterated 65,000 times for good convergence. Note that the proposed ESVT method only needed five calculations and the complexity of each calculation decreased gradually.…”

Section: Experimental Validationmentioning

confidence: 99%

“…In recent decades, structural damage evaluation has become a key issue in the field of civil engineering, mechanical engineering, aerospace engineering and so on. The method based on natural frequency changes [1,2,3,4,5,6,7,8,9,10,11] is one of the mainstream methods for structural damage evaluation, since the natural frequencies are most easily and accurately measured in comparison with other dynamic characteristics of a structure. Messina et al [3] proposed a damage detection method termed the multiple damage location assurance criterion by using the natural frequency sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Ding et al [7] presented an improved artificial bee colony algorithm for crack identification in beam structure. Krishnanunni et al [8] defined an objective function using the frequency sensitivity equation and minimized it using a cuckoo search algorithm to evaluate structural damage. Choi and Han [9] studied frequency-based damage detection in cantilever beam by using a vision-based monitoring system with a motion magnification technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Singular Value Truncation Method for Non-Destructive Evaluation of Structural Damage Using Natural Frequencies

Yang

Wang

et al. 2019

Materials

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As natural frequencies can be easily and accurately measured, structural damage evaluation by frequency changes is very common in engineering practice. However, this type of method is often limited by data, such as when the available natural frequencies are very few or contaminated. Although much progress has been made in frequency-based methods, there is still much room for improvement in calculation accuracy and efficiency. To this end, an enhanced singular value truncation method is proposed in this paper to evaluate structural damage more effectively by using a few lower order natural frequencies. The main innovations of the enhanced singular value truncation method lie in two aspects: The first is the normalization of linear systems of equations; the second is the multiple computations based on feedback evaluation. The proposed method is very concise in theory and simple to implement. Two numerical examples and an experimental example are employed to verify the proposed method. In the numerical examples, it was found that the proposed method can successively obtain more accurate damage evaluation results compared with the traditional singular value truncation method. In the experimental example, it was shown that the proposed method possesses more precise and fewer calculations compared with the existing optimization algorithms.

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Damage Detection in T-Beam Bridges Using Transmissibility of Vibration Signals

Lakshmi¹,

Devi²

2022

Lecture Notes in Civil Engineering

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Sensitivity-based damage detection algorithm for structures using vibration data

Cited by 24 publications

References 18 publications

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Enhanced Singular Value Truncation Method for Non-Destructive Evaluation of Structural Damage Using Natural Frequencies

Damage Detection in T-Beam Bridges Using Transmissibility of Vibration Signals

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