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地应力是边坡稳定性分析的重要因素。在杏山铁矿露天高陡边坡实测地应力数据的基础上,采用多元线性回归方法,在充分考虑岩体自重与构造应力影响的情况下,对5类11种边界条件工况进行了有限差分FLAC3D的模拟加载,反演得出研究区域内的地应力场。鉴于该方法只能对地应力场进行线性和全区反演,这对内部结构极其复杂、矿区内各个区域岩性和地形差异较大的金属矿山矿体显然是不够准确的。为了克服以上缺陷,采用了非线性的神经网络反演方法,并根据岩体的岩性分布和地形起伏将整个矿区划分为5个分区,通过引入侧压力系数k0实现分区域反演,从而得到整个矿区的地应力场。研究表明,在复杂地质条件下,神经网络方法反演出的初始地应力分布更加合理。
Ground stress is an important factor in slope stability analysis. On the basis of the measured geostress data of the open steep slope in the Xingshan Iron Mine, using the multiple linear regression method, under the condition of fully considering the influence of the rock mass’s own weight and tectonic stress, the operating conditions of the five types of 11 boundary conditions are limited In the simulated FLAC3D loading, the in-situ stress field in the study area is obtained by inversion. In view of the fact that this method can only perform the linear and total area inversion of the geostress field, it is extremely complicated to the internal structure. It is obviously not accurate enough for the metal ore body with large difference of lithology and topography in each area in the mining area. In order to overcome these shortcomings, a nonlinear neural network inversion method is adopted. According to the lithology distribution and topography of the rock mass, the whole mining area is divided into five zones. By introducing the lateral pressure coefficient k0, subregional inversion can be achieved. The stress field of the whole mining area. The research shows that under complex geological conditions, the initial geostress distribution inverted by the neural network method is more reasonable.