基于位移增量的高地应力下硐室群围岩蠕变参数的智能反分析

深部高地应力下硐室围岩蠕变力学参数不准已成为制约岩体工程理论分析和数值计算发展的“瓶颈”问题,因而确定可靠的岩体蠕变参数服务于工程实际成为重点关注的课题,为此提出基于位移增量敏感性分析的高地应力下硐室群围岩蠕变参数的智能反分析方法。该方法基于描述深部高地应力条件下围岩蠕变随时间破坏特征的分数阶微积分的蠕变本构模型,取较为典型的主硐室顶拱位移、拱肩位移和边墙位移作为表征围岩的特性指标,从参数敏感性分析着手,确定模型中对位移较为敏感的5个蠕变参数(瞬时剪切模量、黏性系数、黏弹性剪切模量、黏弹性系数、分数阶系数β),并遵循“大值原则”和“敏感性原则”提取位移增量数据,输入主厂房和主变室等不同位置的现场位移增量监测信息建立多数据融合的适应度函数,采用均匀设计方法构造各参数不同水平组合的学习样本和训练样本,通过遗传算法与神经网络相结合的智能优化算法在解的全局空间进行搜索,确定反演蠕变参数值,最终通过灰色关联度和后验差法联合校核位移增量实测值与计算值。实例分析结果不仅表明智能优化算法获得的硐室围岩蠕变参数可靠性高,也验证了该法的有效性和合理性,同时为深部高地应力大型硐室长期稳定性评价时参数确定提供一个新手段。

Intelligent back analysis of rock mass creep parameters for large underground caverns under high in-situ stress based on incremental displacement

The creep mechanical parameters inaccuracy of surrounding rock under high in-situ stress have become the “bottleneck” problem on the theoretical analysis and numerical calculation,therefore,determining the reliable rock mass creep parameters is urgently needed to be solved in practical engineering.An intelligent back analysis method of rock mass creep parameters based on the displacement increment sensitivity analysis for large underground caverns under high in-situ stress is put forward in this paper.This method is to use the fractional order calculus creep constitutive model which can describe the failure characteristics of surrounding rock creep over time under the high ground stress.Taking the displacements of arch roof,arch shoulder and sidewall as characteristic indices,five creep parameters (instantaneous shear modulus,viscosity coefficient,viscoelastic shear modulus,Viscoelastic coefficient,β) under inversion are confirmed by sensitivity analysis.Displacement increment data were extracted following the principle of “great value” and “sensitivity”.Fitness function of displacement increment is established by determining the inversion creep parameters inputting the eight incremental displacement monitoring information from the main power house and main transformer chamber.The construction of different level combination of learning samples and training samples of each parameter by using uniform design method,parameters value is confirmed by adopting genetic algorithm-neural network searching in the global space.Finally the comparative analysis on displacement increment measured values and calculated values is conducted through grey correlation analysis method and posteriori difference method.The results from underground powerhouse of Jinping II hydropower station show that the surrounding rock mass creep parameters are accurate,validated and rational.It also provides a new method for parameters determination during the long-term stability evaluation of large cavern under high in-situ stress.