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在高心墙堆石坝应力变形控制数值模拟中,除了所采用土体本构模型的误差影响外,材料参数取值是否合理也是影响堆石坝变形预测精度的关键因素之一。传统参数反分析方法未考虑材料参数之间相关性影响,可能导致反分析参数值与材料实际特性不匹配。另外,高心墙堆石坝材料分区较多,模型待反分析参数多,计算量较大。针对邓肯–张E-B模型参数,通过大量室内试验数据统计分析,得到材料的抗剪强度参数φ与φ、切线模量系数K_e与体积模量系数K_b、切线模量指数n与m存在显著相关关系,根据其相关关系确定模型基础反分析参数为φ、R_f、K_e和n。构建了基础反分析参数为自变量,数值计算位移值为因变量的响应面方程,运用改进的遗传算法和已知的参数回归方程求得文中模型待反分析参数最优解。以瀑布沟砾石土心墙堆石坝工程为例,对坝体及覆盖层材料参数进行了反分析。计算结果表明,考虑参数相关的反分析方法得到的材料参数更加符合材料的真实特征,由于减少了待反分析参数个数,使计算效率显著提高,该反分析方法可为类似工程提供参考,具有工程应用价值。
In numerical simulation of stress and deformation control of rockfill dam with high core, besides the error influence of soil constitutive model adopted, whether the material parameter is reasonable or not is one of the key factors that affect the deformation prediction accuracy of rockfill dam. The traditional parametric back analysis method does not consider the influence of the correlation between the material parameters, which may lead to mismatch between the inverse analysis parameters and the actual properties of the material. In addition, high core rockfill dam material partition more models to be analyzed more parameters, a larger amount of computation. According to the Duncan-Chang EB model parameters, the shear strength parameters φ and φ, the tangent modulus coefficient K_e and the bulk modulus coefficient K_b and the tangent modulus index n and m of the material are obtained through statistical analysis of a large number of indoor test data , According to their correlation to determine the basis of the model inverse analysis parameters φ, R_f, K_e and n. The basic inverse analysis parameters are constructed as independent variables, and the displacement value is calculated as the response surface equation of the dependent variable. The optimal genetic algorithm and the known regression equation are used to obtain the optimal solution of the model to be analyzed. Taking the waterfall ditch gravel core wall rockfill dam project as an example, the material parameters of the dam body and the overburden are analyzed inversely. The calculation results show that the material parameters obtained by the parameter-dependent back analysis method are more in line with the true features of the material. As the number of parameters to be analyzed is reduced, the computational efficiency is significantly improved. The inverse analysis method can provide reference for similar projects and has Engineering application value.