隧洞围岩损失位移估计的智能优化反分析  

An intelligent optimization method of back analysis for loss displacement of surrounding rocks of tunnel

作　　者：张研[1,2] 苏国韶[1,3] 燕柳斌[1] 

机构地区：[1]广西大学土木建筑工程学院,南宁530004 [2]广西大学工程防灾与结构安全教育部重点实验室,南宁530004 [3]广西大学广西防灾减灾与工程安全重点实验室,南宁530004

出　　处：《岩土力学》2013年第5期1383-1390,共8页Rock and Soil Mechanics

基　　金：国家自然科学基金项目(No.51069001);广西理工科学实验中心重点项目(No.LGZX201001);广西重点实验室系统性研究项目(No.2012ZDX10)

摘　　要：隧洞开挖过程中围岩监测断面的布置一般滞后于掌子面开挖,监测断面布置前围岩已发生的位移称为损失位移。采用优化反分析思路求取损失位移,该思路将损失位移的求解转化为以实测位移与计算位移的误差作为目标函数、岩体力学参数作为决策变量的全局优化反分析问题。针对该全局优化反分析问题是一类高度非线性多峰值且计算代价较高的优化问题,将性能优异的粒子群优化算法与高斯过程机器学习方法相融合,结合FLAC3D数值计算程序,提出隧洞围岩损失位移优化反分析的粒子群-高斯过程-FLAC3D智能协同优化方法。算例研究表明,该方法是可行的,不仅能获得可靠的损失位移预测结果,而且可获取合理的围岩计算模型力学参数,具有全局性好、计算效率高的特点,克服了传统优化反分析方法容易陷入局部最优或过于依赖初始学习样本的局限性。将该方法应用到锦屏二级水电站辅助洞BK14+599断面的损失位移反分析,获得了该断面围岩的损失位移和力学参数,其中,损失位移较大,原因在于岩体开挖后在短时间内弹性变形大。因此,对于地下工程,特别是深部地下岩体工程,在围岩稳定性评价与围岩参数反分析中,损失位移不可忽视,应给予足够重视。The monitored sections are always assembled behind working face excavation. The displacement induced during this period is called loss displacement. The optimization back analysis method is used to get loss displacement. The method transforms the problem to a global optimized problem that treats the error between geodesic loss displacement and computational loss displacement as objective fimction, the mechanical parameters of surrounding rocks as decision variables. Aiming to solve the global optimized problem that is high nonlinearity, many peak values and expensive cost, an intelligent cooperative optimization algorithm based on particle swarm optimization （PSO） and Gaussian process （GP） machine learning for back analysis is proposed, then combined the FLAC30, a new method called PSO-GP-FLAC30 for the loss displacement back analysis is developed. The results of a numerical example show that the proposed method is feasible. It not only obtains reliably predicted loss displacement, but also gets reasonable mechanical parameters of surrounding rocks. In addition, the proposed method has the merits of global optimization and high computational efficiency. It can overcome the shortcomings that the traditional optimization back analysis method is easy to fall into local optimum or overly dependent on initial learning samples. The proposed method is applied to the auxiliary tunnel BK14＋599 section of Jinping II hydropower station in China, and loss displacement and mechanical parameters of surrounding rocks are obtained. The results indicate that the elastic deformation of surrounding rocks increased quickly after excavation, which results in large loss displacement. Therefore, the loss displacement of surrounding rocks can not be ignored in stability evaluation or back analysis for underground engineering, especially for deep underground rock engineering.

关 键 词：隧洞 损失位移 反分析 粒子群算法 高斯过程 

分 类 号：TV314[水利工程—水工结构工程] TU457[建筑科学—岩土工程]