Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters

Abstract

In the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fragmentation after blasting is quantified by adopting analytic hierarchy process (AHP). On this basis, the shaft blasting maximum rock fragmentation control model based on double hidden layer BP neural network is proposed. Results show that the maximum rock fragmentation discharged from the mucking shaft after blasting should not exceed 1/3 of the diameter of the slag chute. The influence weight of the minimum resistance line that accounts to 15.6%, when AHP is applied for the quantification of the blasting parameters, can be regarded as the most important blasting parameter. The average absolute errors between the predicted value and the actual value of the largest block size control model of the shaft blasting are only 2.6%. The inversion analysis of the model can rapidly obtain the required blasting parameters, which can be used to guide the construction of the tunnel ventilation shaft.

Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics

“…Li Qingxiang and his colleagues conducted a quantitative analysis of the impact of blasting settings on rock fragmentation. They also developed a control model using a double-hidden-layer BP neural network, as described in the study by Li [ 31 ]. These studies emphasize the significance of blast design factors in attaining the intended rock fragmentation results.…”

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

“…For this reason, the function S (a, W) can be define R, 0 < a < 2R, make S obtain the maximum value as far as possible and the p determined w to determine the optimal value of a. According to the geometric ship in Figure 9, the explicit function expression of hole network area S (a, W) c tained: Because: 1 4 a 2 + W 2 = R 2 , in order to maximize the value of pore network area s, let the first derivative of s be zero, and according to the empirical calculation:…”

“…The fragmentation of blasting is an important indicator for evaluating the effect of blasting. Scholars have carried out a lot of research on the theory of fragmentation of rock blasting and have summarized a variety of control methods [1]. Shi Xiaopeng systematically studied the effect of blasting vibration on the filling body of large-diameter deep holes in Anqing copper mine using regression calculations and theoretical analysis, and then proposed technical measures to reduce the vibration intensity of large-diameter deep holes in Anqing copper mine based on the actual situation of blasting production [2].…”