摘要
建立了沉陷反分析的神经网络模型 ,并用基于正交试验获得的训练样本对网络进行学习 ,以此训练好的神经网络模型来描述岩体力学参数与开采沉陷之间的关系 ,利用反演结果 ,建立拉格朗日快速计算法 (FLAC)模型 ,对地表沉陷进行预测 。
An ANN model for inversing mechamical parameters of rock mass from measured surface subsidence induced by underground mining has been established.The network was trained with input-output data pairs obtained from FLAC simulation based on the orthogonal tests.The trained network provided the relation between mechanical parameters of the rock mass and the surface subsidence induced by underground mining and was used to inverse the mechanical parameters of the rock mass.The inversion results were in turn used as input parameters of a FLAC model predicting the mining-induced surface subsidence.The prediction was in good agreement with the measured subsidence.
出处
《南华大学学报（理工版）》
2001年第1期10-14,共5页
Journal of Nanhua University(Science & Engineering)
