基于BP神经网络的岩体质量评价——以甘肃北山旧井地段BS03号钻孔为例

Rock mass quality assessment based on BP artificial neural network (ANN)——a case study of borehole BS03 in Jiujing segment of Beishan,Gansu

岩体质量评价对于各类建筑特别是地下建筑的安全性具有十分重要的作用。文章利用岩体质量评价Q系统和BP神经网络分析了Q系统各参数及对应于BP网络的取值。之后,将研究对象——甘肃北山BS03号钻孔以10 m为单位分成50段,对每段范围内岩心所对应Q系统的参数根据钻孔钻进结果进行赋值,并运行BP神经网络程序对钻孔附近岩体质量进行分类。通过评价,对研究对象周围的岩体质量给出了定性的结论。

Rock mass quality assessment plays an important role in the security for all kinds of architectures, especially for the underground project. In this paper, the author made an analysis on the features of Quantitative Project Classification Q - system and BP artificial neural network, then taking Borehole BS03 as example, quantified the parameters of Q - system according to the quantify rule of the six parameters, finally, ran the BP ANN program to calculate the parameter of neighbor rock mass and made qualitative assessment of the rocks around Borehole BS03.