Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

Zhou, Jian; Guo, Haoxu; Koopialipoor, Mohammadreza; Armaghani, Danial Jahed; Tahir, Mahmood Md.

doi:10.1007/s00366-019-00908-9

Cited by 82 publications

(25 citation statements)

References 81 publications

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“…Brittleness can be the reason behind numerous disastrous incidents associated with rock mechanics, such as rock-bursts [6][7][8][9]. According to the literature, in the prediction performance of the tunnel boring machines (TBMs) and roadheaders, brittleness can be taken into account as a significant and effective factor [10,11].…”

Section: Introductionmentioning

confidence: 99%

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

et al. 2020

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The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.

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Section: Introductionmentioning

confidence: 99%

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

et al. 2020

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“…Among ANN, the multi-layer perceptron (MLP) was considered to be the best ANN architecture and contains five parts, including the input layer, the hidden layer, the output layer, neurons in the layer and weight among these neurons. The back-propagation (BP) algorithm is the most popular MLP, and the knowledge form the training datasets will be forwarded, while the errors will be passed back to guide the updating of weights value [55][56][57]. During BP training, the weight updated according to the cost function for achieving better training performance, and that process will be ended when the cost function equal to or less than the setting level [58].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Shi

Zhou

et al. 2020

Applied Sciences

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A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

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“…Another hybrid model of least squares SVM-PSO was developed by Wu et al [73] to determine a non-linear relationship between the model input parameters and RB and to evaluate the risk associated with the RB. The risk level of RB was evaluated and predicted in the study conducted by Zhou et al [74] using another model, which was a combination of the firefly algorithm and ANN. Zhou et al [74] showed that their model could evaluate the risk level of the RB with a high degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The risk level of RB was evaluated and predicted in the study conducted by Zhou et al [74] using another model, which was a combination of the firefly algorithm and ANN. Zhou et al [74] showed that their model could evaluate the risk level of the RB with a high degree of accuracy. In another research Pu et al [75] investigated the likelihood of occurrence of RB in an igneous rock type (i.e., kimberlite) using a combination of principal component analysis and a fuzzy model and concluded that their proposed model could perform well in the field of RB occurrence.…”

Section: Introductionmentioning

confidence: 99%

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

Khandelwal²,

Asteris³

et al. 2021

IEEE Access

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Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.

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Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

Cited by 82 publications

References 81 publications

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

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