Rock burst prediction probability model based on case analysis

Wu, Shunchuan; Wu, Zhongguang; Zhang, Chenxi

doi:10.1016/j.tust.2019.103069

Cited by 94 publications

(33 citation statements)

References 33 publications

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“…The vibration is a semisine wave, the frequency is 20 Hz, and the vibration time is that for one cycle [29][30][31][32]. Wu et al [33] analyzed the rock-burst potential of roadway under different buried depth conditions. The hazard level of roadway depth and rock-burst potential is shown in Table 3.…”

Section: Simulation Methodologymentioning

confidence: 99%

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Numerical Analysis of Roadway Rock-Burst Hazard under Superposed Dynamic and Static Loads

Kong

Jiang

et al. 2019

Energies

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Microseismic events commonly occur during the excavation of long wall panels and often cause rock-burst accidents when the roadway is influenced by dynamic loads. In this paper, the Fast Lagrangian Analysis of Continua in 3-Dimensions (FLAC3D) software is used to study the deformation and rock-burst potential of roadways under different dynamic and static loads. The results show that the larger the dynamic load is, the greater the increase in the deformation of the roadway under the same static loading conditions. A roadway under a high static load is more susceptible to deformation and instability when affected by dynamic loads. Under different static loading conditions, the dynamic responses of the roadway abutment stress distribution are different. When the roadway is shallow buried and the dynamic load is small, the stress and elastic energy density of the coal body in the area of the peak abutment stress after the dynamic load are greater than the static calculations. The dynamic load provides energy storage for the coal body in the area of the peak abutment stress. When the roadway is deep, a small dynamic load can still cause the stress in the coal body and the elastic energy density to decrease in the area of the peak abutment stress, and a rock-burst is more likely to occur in a deep mine roadway with a combination of a high static load and a weak dynamic load. When the dynamic load is large, the peak abutment stress decreases greatly after the dynamic loading, and under the same dynamic loading conditions, the greater the depth the roadway is, the greater the elastic energy released by the dynamic load. Control measures are discussed for different dynamic and static load sources of rock-burst accidents. The results provide a reference for the control of rock-burst disasters under dynamic loads.

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Section: Simulation Methodologymentioning

confidence: 99%

“…Therefore, it is meaningful to study the rock-burst potential under different superposed dynamic and static loads Table 3. Quantitative assessment of the impact of seismicity on the rock-burst potential [27,33]. During the static analysis, the boundary of the model is fixed.…”

Section: Simulation Methodologymentioning

confidence: 99%

Numerical Analysis of Roadway Rock-Burst Hazard under Superposed Dynamic and Static Loads

Kong

Jiang

et al. 2019

Energies

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“…He then successfully trained a model based on the Support Vector Machine algorithm. Wu (2019) used the Least Squares Support Vector Machine algorithm to create a rockburst forecast model and by conducting sensitivity analyses reported that the ratio of tangential stress to the uniaxial compressive strength has the greatest influence on the forecast. used the Logistic Regression algorithm in a database consisting of rockburst and non-rockburst incidents.…”

Section: Machine Learning In Rockburst Predictionmentioning

confidence: 99%

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Papadopoulos

Benardos

2021

Geotech Geol Eng

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One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst risk assessment with the use of machine learning is currently gaining increased attention, due to the fact that outperforms the widely used empirical approaches. However, the limited and imbalanced instance records, combined with the multiparametric nature of the phenomenon, can lead to unstable estimations. This study focuses on the enhancement of the prediction performance of five machine learning algorithms, including Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, by utilizing the oversampling technique SMOTE (Synthetic Minority Oversampling TEchnique).The initial database consists of 249 rockburst incidents, from which approximately 70% was used as the training set and the remaining 30% as the test set. Parametric analyses were conducted regarding different indicator combinations, such as the maximum tangential stress, the rock's uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. The models were trained with the original dataset and afterwards a gradual increase of the database with synthetic instances was made until the obtainment of a balanced dataset. Subsequently the creation of synthetic instances was continued until the real incidents used for training and the synthetic incidents were of the same amount. The results from the following analysis show that SMOTE technique has a considerable effect in the evaluation metrics of the models, even after the balancing of the dataset, and can be a valuable asset for the rockburst prediction.

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“…Zhou et al [1] introduced three hybrid support vector machine (SVM) models which were optimized using heuristic algorithms i.e., genetic algorithm, grid search method, and particle swarm optimization (PSO) for determining the RB. 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.…”

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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Rock burst prediction probability model based on case analysis

Cited by 94 publications

References 33 publications

Numerical Analysis of Roadway Rock-Burst Hazard under Superposed Dynamic and Static Loads

Numerical Analysis of Roadway Rock-Burst Hazard under Superposed Dynamic and Static Loads

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

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