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为了开采阿舍勒铜矿上下中段大体积充填体之间的隔离中段矿体,对隔离中段的采场结构参数进行了研究。结合矿山实际情况,对采场结构参数进行了多种方案的FLAC3D数值模拟,采用神经网络和遗传算法对模拟结果进行选择、优化,确定了最佳的采场结构参数。结果表明:将神经网络和遗传算法结合起来,利用FLAC3D数值模拟的计算结果,以充填体的破坏率为目标函数,取得了理想的优化效果,实现了采场结构参数值的连续不间断优化,很好地弥补了数值模拟的缺点。
In order to exploit the isolated mid-section ore body between the upper and lower mid-section large-scale backfill of Ashele copper mine, the stope structure parameters in the mid-section of isolation were studied. Combined with the mine actual situation, a lot of schemes FLAC3D numerical simulation of stope structure parameters were carried out, the simulation results were selected and optimized by neural network and genetic algorithm to determine the best stope structure parameters. The results show that the neural network and genetic algorithm are combined and the optimization results are obtained by using FLAC3D numerical simulation results and the failure rate of the filling body as the objective function. The continuous uninterrupted optimization of stope structural parameters is achieved, Good to make up for the shortcomings of numerical simulation.