Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.
The secant modulus of the rock reflects the stiffness of the rock and the ability to resist deformation. There are significant differences in the secant modulus of the rock due to the different numbers of joints in the rock and the change in rock size. Therefore, it is important to obtain effectively the secant modulus of rocks with the number of parallel joints for evaluating rock deformation. In this study, the method of regression analysis is used, and 10 sets of numerical plans are set up to discuss the influence of a number of parallel joints and rock size on the secant modulus. The results show that the secant modulus decreases with the increase in a number of parallel joints, and the curve is a power function. The secant modulus decreases when the rock size increases, and the curve is an exponential function. The characteristic secant modulus and characteristic size decrease with the increase in the number of parallel joints, and their curves are an exponential function. The specific forms of these relationships are given in the article. The establishment of these relationships realizes the prediction and calculation of the rock secant modulus with the number of parallel joints, which provides a guiding significance for the rock deformation analysis.
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