Evaluation of rock bolt integrity using Fourier and wavelet transforms

Lee, In Mo; Han, Shin In; Kim, Hyun Jin; Yu, Jung-Doung; Min, Bok Ki; Lee, Jong Sub

doi:10.1016/j.tust.2011.11.009

Cited by 42 publications

(24 citation statements)

References 14 publications

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“…In order to obtain the effective signal, many data processing methods, such as the short-time Fourier transform, the Gabor transform, the Wigner-Ville transform, the wavelet transform and so on, are proposed. Wavelet transform is the most used signal analysis method among them [14][15][16][17][18] . However, the effect of the wavelet transform is often limited by the wavelet base, as well as the number of decomposed layers.…”

Section: Introductionmentioning

confidence: 99%

Bolt Detection Signal Analysis Method Based on ICEEMD

Guo

Zhang

Xie

et al. 2018

Shock and Vibration

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The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold de-noising technique. Based on the approximate entropy, and the wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions, and also that the IMF can effectively identify the reflection signal of the end of the bolt.

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Section: Introductionmentioning

confidence: 99%

Bolt Detection Signal Analysis Method Based on ICEEMD

Guo

Zhang

Xie

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…The dilative mother wavelet corresponds to low-frequency resolution. The compressive mother wavelet corresponds to high-frequency resolution [18]. By calculating the cross-correlation of the original signal ( ) z t and the mother wavelet function under different scales, the frequency components are obtained.…”

Section: Wavelet Transformmentioning

confidence: 99%

Detecting the Void behind the Tunnel Lining by Impact-Echo Methods with Different Signal Analysis Approaches

Cao

Liang

et al. 2019

Applied Sciences

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A void behind the lining in a tunnel is considered to be a critical condition as it can significantly impair the tunnel service life. In this study, we adopted the impact-echo (IE) method to detect the voids. We designed two test conditions (tunnel lining with and without a void) for our experiments performed in a laboratory environment. The influences of void size and impact-void position were analysed using numerical simulations. The vibration response signals were analysed in the time, frequency, and time-frequency domains using various signal analysis approaches. The results were comparatively analysed to determine the best approach for void detection. The study helped establish that a tunnel void can be evaluated through the vibration energy (amplitude and duration) in the time domain, the resonance frequency and dynamic stiffness in the frequency domain, and the energy distribution in time-frequency domain. The wavelet transform analysis is the most appropriate method to observe the energy flow during the state changing and the dynamic stiffness method can determine the void position precisely.

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“…Due to the nonlinearity of loose zone for surrounding rock and uncertainty of state and parameter measurement, the prediction of surrounding rock loose zone can be regarded as a typical complex and fuzziness problem. In recent decades, various intelligent methods, such as artificial neural network (ANN) [24][25][26], fuzzy logic [27][28][29], genetic algorithm (GA) [30,31], wavelet packet analysis (WPA) [32][33][34], supporting vector machine (SVM) [35][36][37], among others [38][39][40], have been developed to deal with nonlinearities and uncertainties. The support vector machine prediction method [41] can solve the problems of nonlinearity and small samples, and has achieved some success in the prediction of roadway surrounding rock loose zone.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

Yang

Wang

et al. 2019

Applied Sciences

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By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method.

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Evaluation of rock bolt integrity using Fourier and wavelet transforms

Cited by 42 publications

References 14 publications

Bolt Detection Signal Analysis Method Based on ICEEMD

Bolt Detection Signal Analysis Method Based on ICEEMD

Detecting the Void behind the Tunnel Lining by Impact-Echo Methods with Different Signal Analysis Approaches

Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

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