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.
Highlights
Long, large, and deep tunnels have been a trend in tunnel construction, and meeting the requirements of tunnel ventilation and fire smoke exhaust using the single ventilation method has been difficult
The double hidden layer BP neural network algorithm combined with specific engineering examples is used to establish the maximum fragmentation control model of shaft blasting
The conclusions are presented as follows: (1) The analysis of the ore pass blockage mechanism determined that the excessively large blasting block is the main cause for the blockage of mucking shaft drilled by Raise boring machine (RBM)
Summary
Large, and deep tunnels have been a trend in tunnel construction, and meeting the requirements of tunnel ventilation and fire smoke exhaust using the single ventilation method has been difficult. Li took the construction of the gas supply shaft at the tail end of the spillway tunnel on the right bank of Xiluodu hydropower station, China, as an example to adjust and control the blasting parameters of the blasting fragmentation to reduce the probability of mucking shaft blockage due to excessive gravel and to ensure the efficiency of the RBM method [14]. This work investigated the mucking shaft blockage mechanism, quantified the blasting parameters of the shaft based on the analytic hierarchy process (AHP) method, combined it with the construction of the Jinhua mountain tunnel ventilation shaft in China, used the double hidden layer BP neural network algorithm to establish the control model of maximum fragmentation of blasting, and evaluated and analyzed the prediction effect and application methods of the model
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