【摘要】 在综合分析条带开采地表下沉系数影响因素的基础上,采用神经网络方法建立了条带开采地表下沉系数的计算模型。模型以国内外成功的条带开采实例为学习训练样本和测试样本,对模型的计算结果与实测值进行了对比分析,分析结果表明,该模型的计算值更接近于实测值。在上述研究的基础上,在给定条带开采采出率的条件下,以条带开采的地表下沉系数最小为原则,运用该模型实现了对条带开采尺寸的优化设计。该研究的成果,为条带开采地表下沉系数的理论计算及条带开采尺寸的优化设计探索出了一种新的方法。更多还原

【Abstract】 Based on comprehensive analysis of the main factors influencing subsidence, the model to calculate subsidence factor in strip mining was set up according to the theory of artificial neural network (ANN). A large amount of successful field cases of strip mining from both at home and abroad was used as learning and training samples to train and test the model. Then the calculated results of the ANN model and the observed values were compared and analyzed. It shows that the results of ANN are closer to the observed values. Based on this model, according to the principle of minimal subsidence factor, the optimal design of strip mining could be realized at given recovery ratio. It provides a new theoretical calculation for the subsidence factor and new design method for optimizing the strip mining scales.更多还原