摘要
为了提高数值计算结果的可靠度,基于正交设计、差分法和人工神经网络建立了新的边坡岩体力学参数反分析方法.按照正交设计要求,选定反演参数的水平,确定数值模拟方案;用FLAC2D差分程序计算得出相应的神经网络分析样本;对RBF神经网络进行训练;利用现场监测位移,对某露天矿边坡岩体的力学参数进行神经网络反分析.反分析结果与理论值的误差很小,满足精度要求,表明该反分析方法的可行性和精确性.
In order to improve the reliability of numerical simulation results, a new backward analysis method for mechanical parameters of slope rocks was developed based on orthogonal design, difference method and artificial neural network. According to orthogonal design, the value levels of the mechanical parameters were chosen, and simulation schemes were arranged; the related analytical samples for neural network were given by FLAC^2D calculations; RBF neural network was trained; the physical and mechanical parameters of an open pit slope were analyzed backwards by well-trained RBF neural network and surveyed data about spot displacements. The error between the backward analysis results and the theoretical ones is much little and meets the demand of precision, which indicates that this backward analysis method is feasible and accurate.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2006年第12期1106-1110,共5页
Journal of University of Science and Technology Beijing
关键词
边坡岩体
力学参数
反分析
正交设计
差分法
神经网络
slope rocks
mechanical parameters
backward analysis
orthogonal design
difference method
neural network
