Evaluation of bedding effect on the bursting liability of coal and coal-rock combination under different bedding dip angles

The progress of construction and safe production in mining, water conservancy, tunnels, and other types of deep underground engineering is seriously affected by rockburst disasters. This makes it essential to accurately predict rockburst intensity. In this paper, the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (σ_θ/σ_c), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (σ_c/σ_t), and the elastic energy index of rock (W_et) were chosen as input indices, and rockbursts were graded as level I (none rockburst), level II (light rockburst), level III (medium rockburst), and level IV (strong rockburst). A total of 104 groups of rockburst engineering samples, collected widely from around the world, were divided into a training set (84 groups of samples) and a test set (20 groups of samples). Based on the kernel principal component analysis (KPCA), the adaptive particle swarm optimization (APSO) algorithm, and the support vector machine (SVM), the KPCA‐APSO‐SVM model was established. The proposed model showed satisfactory classification performance: the prediction accuracies of the training set and test set were 98.81% and 95%, respectively. In addition, the trained prediction model was applied to five rockburst engineering cases and compared with the BP neural network model, SVM model, and APSO‐SVM model. The comparative results show that the KPCA‐APSO‐SVM model has a higher prediction accuracy; as such, it provides a new reliable method for rockburst prediction.

As an inherent property of the accumulation of elastic energy and the sudden instability failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. To accurately classify the CBL level, the support-vector-machine (SVM) method was introduced in this paper, and the dynamic failure time (DT), elastic energy index (W_ET), impact energy index (K_E) and uniaxial compressive strength (R_C) were selected as the classification indexes. An imbalanced sample set, containing 95 groups of measured data of CBL, was established, and eight SVM classification models were constructed, based on different kernel functions and swarm-intelligence-optimization algorithms. Focusing on the problem of sample imbalance, the classification accuracy, A, F1-score and kappa coefficient were used to comprehensively evaluate the classification performance of SVM models, and the grey-wolf-optimizer SVM (GWO-SVM) model was selected as the best model in this paper, reaching the highest accuracy of 98.9%. The GWO-SVM was applied to identify the CBL level of the 4# coal seam in Xiaozhuang Coal Mine and the 1# coal seam in the Wanfeng Coal Mine. The results of the engineering application are consistent with those from the engineering field, and show that the proposed model is scientific and practical, and can be a new method for CBL classification.

Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation (BP) neural network has been widely used in rockburst prediction. However, there are few reports about the influence study of different training sample sizes, optimization algorithms and index dimensionless methods on the prediction accuracy of BP neural network models. Therefore, 100 groups of typical rockburst engineering samples were collected locally and abroad, and considering the relevance, scientificity and quantifiability of the prediction indexes, the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (σ_θ/σ_c), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (σ_c/σ_t) and the elastic energy index (W_et) were chosen as the prediction indexes. When the number of samples was 40, 70 and 100, sixty improved BP models were established based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, Levenberg–Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, normalized processing method). The prediction performances of each improved model were compared with those of standard BP models. The comparative study results indicate that the sample size, optimization algorithm and dimensionless method have different effects on the prediction accuracy of BP models, which are described as follows: (1) The prediction accuracy value A of the BP model increases with the addition of sample size. The average value A_ave of twenty improved models under three kinds of sample sizes increases from A_ave (40) = 69.7% to A_ave (100) = 75.3%, with a maximal value A_max from A_max (40) = 85.0% to A_max (100) = 97.0%. (2) The value A and comprehensive accuracy value C of the BP model based on four optimization algorithms are generally higher than those of the standard BP model. (3) The improved BP model based on the unified extreme value processing method combined with the Levenberg–Marquardt algorithm has the highest value A_max (100) = 97.0% and value C = 194, and the prediction results of five engineering cases are completely consistent with the actual situation at the site, so this is the best BP neural network model selected in this paper.

With the increased mining depth, the dynamic disaster of rock burst in coal mines has become increasingly prominent, and the impact tendency of coal and rock mass in deep coal seam mining is a necessary condition for the occurrence of rock burst and an important index to measure the failure of coal and rock mass. Laboratory tests and numerical tests were used to study the impact tendency of coal and roof strata, including the deformation characteristics, failure characteristics, and bending energy index of the coal and rock mass of different sizes, the failure law and energy evolution characteristics of tlhe coal and rock mass under the same size, and the unloading characteristics of the coal and rock mass under the same size and different confining pressures. The results are shown as follows: (1) The rock roof was determined to have a weak impact tendency through the mechanical test. (2) With the increased size, the microcracks in the rock samples increased correspondingly, and the increased meso‐defect leading to the increased heterogeneity was an essential reason for the size effect. The strength of the rock mass decreased with the increased specimen size. The larger the specimen size was, the lower the bending energy index was. (3) Triaxial loading and unloading were tested for the same size under different surrounding rocks. Under the same loading conditions, with the increased confining pressure, the strength and bending energy index of rock mass increased correspondingly, and the failure of rock mass transformed from tensile to shear failure. The failure form and strength characteristic of rock under the unloading condition are different from those under the loading condition. The failure degree was intense, with a high bending energy index. Compared with the loading situation, the impact tendency caused by unloading was higher, and the dynamic impact disaster was more likely to occur.