Prediction and Assessment of Rock Burst Using Various Meta-heuristic Approaches

Shukla, Ramesht; Khandelwal, Manoj; Kankar, Pavan Kumar

doi:10.1007/s42461-021-00415-w

Cited by 28 publications

(12 citation statements)

References 38 publications

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“…(1) So far, many methods are used to predict mine dynamic disasters [5,[28][29][30][31]. Compared with others, terrain curvature method has the following characteristics.…”

Section: Discussionmentioning

confidence: 99%

Control and Application of Terrain Curvature on Dynamic Disasters: A Case Study of Coal and Gas Outbursts in the Pingdingshan Mining Area, China

Sun

Chen

et al. 2021

Advances in Civil Engineering

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Based on coal and gas outburst mining area topography, a risk evaluation index for mine dynamic disasters in relation to terrain curvature is presented by analyzing the terrain curvature formation and determining its classification and computational methods. Clarifying the mechanism that the terrain curvature changes reflects the intensity of tectonic activity and the tectonic stress field variation, as well as coal and rock mass energy storing. By using the N-shaped landform in the Pingdingshan coal mine as a case study, an area having strong tectonic activity and coal and gas outbursts was recorded to occur where terrain curvature changed, notably occurring where the positive curvature radius was 80.21% of the total. These findings indicate that terrain curvature has significant controlling effects on coal and gas outbursts. A preliminary assessment for the occurrence risk of coal and gas outbursts using the terrain curvature value is proposed which can be used as an effective method to predict future outbursts.

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“…(1) So far, many methods are used to predict mine dynamic disasters [5,[28][29][30][31]. Compared with others, terrain curvature method has the following characteristics.…”

Section: Discussionmentioning

confidence: 99%

Control and Application of Terrain Curvature on Dynamic Disasters: A Case Study of Coal and Gas Outbursts in the Pingdingshan Mining Area, China

Sun

Chen

et al. 2021

Advances in Civil Engineering

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show abstract

“…Where the point (x, μ i j (x)) is called cloud drop, and it can be obtained on basis of the digital features (Ex, En, He, a', k), which can be specified by Eqs. from (8) to (12). Where, En i j and He i j are the entropy and hyper entropy of the grade i for indicator j, respectively.…”

Section: B Connection Cloud Modelmentioning

confidence: 99%

“…Then, based on classification standards, calculate the left and right half intervals widths and the digital feature values (Ex, En, He, a', k) by Eqs. (8) to (14). Next, generate N cloud drops based on digital features to simulate evaluation standard, and calculate the connection degree of the sample responding to each classification grade.…”

Section: A Basic Evaluation Principlementioning

confidence: 99%

“…Therefore, credible and effective prediction of rockburst has great significance to underground engineering. At present, scholars have proposed various prediction methods based on the actual measurement data and experimental results of rockburst and related theories, such as set pair analysis [1], idea point method [2], extension comprehensive evaluation [3], distance discrimination method [4], projection pursuit method [5], fuzzy sets method [6], machine learning method [7], metaheuristic approaches [8], unascertained measure theory [9], and so on. However, due to the complexity of rockburst and the shortcomings of the above-mentioned methods, it is still necessary to explore new rockburst prediction methods.…”

Section: Introductionmentioning

confidence: 99%

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A Connection Cloud Model Coupled With Improved Conflict Evidence Fusion Method for Prediction of Rockburst Intensity

et al. 2021

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As a common geological hazard in underground engineering, rockburst has many uncertain factors and interactions, and is of a complicated mechanism. Based on the information fusion theory, a methodology using a novel connection cloud model, which can overcome the defect of the traditional cloud model requiring factors to be normal distribution when determining the basic probability assignment function of the evidence is proposed for predicting the rockburst intensity. And concepts of Lance-distance and Deng-entropy are also introduced here to improve the traditional evidence fusion algorithm. The practical application shows that the result obtained from the proposed model is consistent with that in the actual site testing, so this model is feasible and effective for the prediction of the rockburst intensity. Furthermore, compared with results from other six improved evidence fusion rules, the improved evidence fusion method proposed here has higher fusion convergence precision, faster convergence rate, and more accurate and reliable results, this may provide a new reference for other similar uncertain problems.

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“…Rockbursts often occur in deep rock mass engineering projects such as tunneling 1 and underground ore body mining 2 , 3 . Catastrophic rockburst can cause rock fragments to fly and rock masses to collapse over a large area, which seriously affects normal construction operations and threatens worker safety.…”

Section: Introductionmentioning

confidence: 99%

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Zhang

Luo

et al. 2022

Sci Rep

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There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor aj is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.

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Prediction and Assessment of Rock Burst Using Various Meta-heuristic Approaches

Cited by 28 publications

References 38 publications

Control and Application of Terrain Curvature on Dynamic Disasters: A Case Study of Coal and Gas Outbursts in the Pingdingshan Mining Area, China

Control and Application of Terrain Curvature on Dynamic Disasters: A Case Study of Coal and Gas Outbursts in the Pingdingshan Mining Area, China

A Connection Cloud Model Coupled With Improved Conflict Evidence Fusion Method for Prediction of Rockburst Intensity

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

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