Search Results (2)

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. Full article

Rockbursts represent hazardous dynamic disasters for underground coal mines and other underground rock engineering projects. Some bursting liability indices are put forward and applied to identify the likelihood of rock burst occurrence. The classification criteria of the bursting liability indices are proved to be reasonable for coals, but they are still immature for non-coal rocks. Thus, it is uncertain that it is reasonable to use the classification criteria of coal for evaluating the bursting liability of non-coal rocks. Hence, in this study, a large amount of data, such as the basic mechanical parameters, i.e., Poisson’s ratio μ, elastic modulus E, uniaxial compressive strength σ_c, and uniaxial tensile strength σ_t, and the bursting liability indices, i.e., elastic strain energy index W_ET, bursting energy index W_cf, dynamic fracture duration time DT, and brittleness index B, of different coals and non-coal rocks were collected in China. Then, the differences of mechanical parameters and rockburst tendency indices between coal and non-coal rocks were studied systematically, and apart from the Poisson’s ratio μ, the other three basic mechanical parameters of coal and non-coal rocks have great differences in data distribution and concentration scope, which proved that the non-coal rocks cannot share the same index system and classification criteria of coals. In addition, the evaluation results of a single index for rock bursting liability of rocks were directly compared in pairs, and the inconsistency rate for coals is about 42–68%. It is necessary to build a comprehensive evaluation method to evaluate the bursting liability of rocks. At last, the modified rockburst tendency classification criteria for non-coal rocks were put forward. It is reasonable to use the classification criteria of the W_ET and W_cf to classify the bursting liability of non-coal rocks, while it is unreasonable to use that of the DT and σ_c. It has been concluded that the index B are more suitable for non-coal rocks, and a new index, named strength decrease rate (SDR), was proposed to determine the bursting liability, which is the ratio of uniaxial compressive strength σ_c to duration of dynamic fracture DT. Full article