基于粒子群优化算法的高地应力条件下硬岩本构模型的参数辨识

PARAMETER IDENTIFICATION OF CONSTITUTIVE MODEL FOR HARD ROCK UNDER HIGH IN-SITU STRESS CONDITION USING PARTICLE SWARM OPTIMIZATION ALGORITHM

粒子群优化(PSO)算法是一类随机全局优化技术,具有收敛速度快、规则简单、易于实现的优点。高地应力条件下硬岩本构模型参数的确定是个尚未解决的难题。以一种适用于高地应力条件下的硬岩本构模型为研究对象,提出基于PSO算法的本构模型参数辨识方法。该方法从本构模型参数的随机值出发,以破坏区的数值计算值与实测值的误差大小作为适应度来评价参数的品质,利用PSO算法规则实现模型参数的进化,搜索出全局最优的模型参数值,从而实现硬岩本构模型参数的自适应辨识。采用该方法对加拿大的Mine-by隧洞和我国的太平驿水电站引水隧洞进行了围岩本构模型参数识别,计算结果与实测情况相吻合,表明该方法是科学可行性的,具有较高的效率和精度。

Particle swarm optimization （PSO） algorithm is a stochastic global optimization technique and has become the hotspot of evolutionary computation because of its excellent performance and simpIicity for implementation. In light of the fact that it is hard to determine the parameters of a constitutive model-cohesion weakening and frictional strengthening （CWFS） model, which performs excellently in modeling the extent and depth of brittle failure zone for hard rock under high in-situ stress condition, a new method is presented to identify parameters of CWFS model using PSO. At first, the stochastic values of parameters are initialized and the difference in failure zone between the value computed and the datum measured is regarded as fitness value to evaluate quality of the parameters. Then the parameters are updated continually using PSO until the optimal parameters are found. Thus parameters are identified adaptively during computation. The results of applications to two real tunnels, i.e., Mine-by tunnel in Canada and Taipingyi tunnel in China, show that the method is feasible and efficient for identifying constitutive parameters and predicting the extent and depth of brittle failure of hard rock under high in-situ stress condition with high precision.