大型地下洞室考虑开挖卸荷效应的位移反分析

Displacement back analysis of rock mechanical parameters of large-scale underground powerhouse with unloading surrouding rockmass

基于现场监测资料的位移反分析是地下工程动态监控、信息化施工的重要组成部分。以乌江彭水水电站大型地下厂房(开挖跨度为30 m,高度为78.5 m)为例,从围岩实测位移出发,建立了基于均匀设计-神经网络-遗传算法的围岩力学参数的系统反分析方法,反演考虑开挖卸荷效应的围岩力学参数。根据数值分析结果形成训练样本,利用BP人工神经网络映射围岩的变形与力学参数的关系,同时针对传统人工神经网络存在初始权值难以确定的问题,应用遗传算法优化神经网络的初始权值;利用现场监测的增量变形反演了围岩的力学参数;最后利用反演出的参数,进行地下厂房开挖预测分析。结果表明,预测位移与现场监测位移较为接近,进行统计检验结果为优,说明该参数反演方法是正确合理的。

Displacement back analysis based on the in-situ measured information is one of the important parts that compose the dynamics measurement-control and information-oriented construction of the underground project.On the basis of measured displacements of surrounding rockmass,a system analysis method for back analysis of rock mechanics mechanical is established,which is used to gain the unloading surrounding rock mechanical parameters of the large-scale underground powerhouse（excavating span 30 meters and height 78.5 meters） of Pengshui Project on Wujiang River.The analysis method includes uniform design（UD）,BP artificial neural network（BP-ANN）and genetic algorithm（GA）.Uniform design and numerical simulation are used to gain training samples of BP-ANN,and BP-ANN is used to simulate the relationship between rock mass displacements and rock mechanical parameters.In addition,the initial input weights of BP-ANN model are optimized by genetic algorithm（GA） because the initial input weights of BP artificial neural network model strongly affect the accuracy of neural network.And the rock mechanical parameters are gained through the trained BP-ANN and measured increment displacement of surrounding rock mass.Finally,the surrounding rock mass displacements induced by the excavation of underground powerhouse are forecasted.According the result,the forecasting displacements are approximate to the measured ones;so as to validate,the rightness and reliability of the above method.