基于随机逆分析方法的土壤力学参数识别

Moussaoui Moufida, Rehab Bekkouche Souhila, Kamouche Houda, Benayoun Fadila, Goudjil Kamel
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引用次数: 1

摘要

土的力学参数必须引入岩土工程计算,特别是那些由有限元法进行的计算,往往是知之甚少。寻找这些参数的数值,使模型最能反映观测到的实际情况,这就是逆分析方法。在本文中,我们感兴趣的是基于逆分析原理的土壤力学参数的识别,利用遗传算法和禁忌搜索混合遗传算法两种随机优化方法。土的特性用本构土莫尔-库仑模型表示。识别涉及以下两个参数:剪切模量(G)和摩擦角(φ)。在德国Hochstetten试验板桩墙上对这两种随机优化方法进行了验证。应用遗传算法方法和混合遗传算法方法辨识两个Mohr-Coulomb参数(G, φ)的结果表明,遗传算法结合禁忌搜索方法的杂交过程加快了算法收敛到问题精确解的速度,而单独使用遗传算法需要更长的计算时间才能达到问题的精确解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Soil Mechanical Parameters by Inverse Analysis Using Stochastic Methods
Abstract The mechanical parameters of the soil that must be introduced into geotechnical calculations, in particular those carried out by the Finite Element Method, are often poorly understood. The search for the numerical values of these parameters so that the models best reflect the observed reality constitutes the inverse analysis approach. In this article, we are interested in the identification of the mechanical parameters of the soil based on the principle of inverse analysis using the two methods of stochastic optimization, the genetic algorithm and the hybrid genetic algorithm with Tabu search. Soil behavior is represented by the constitutive soil Mohr-Coulomb model. The identification relates to the following two parameters: The shear modulus (G) and the friction angle (φ). The validation of these two stochastic optimization methods is done on the experimental sheet pile wall of Hochstetten in Germany. The results obtained by applying the genetic algorithm method and the hybrid genetic algorithm method for the identification of the two Mohr-Coulomb parameters (G, φ) show that the hybridization process of the genetic algorithm combined with the Tabu search method accelerated the convergence of the algorithm to the exact solution of the problem whereas the genetic algorithm alone takes a much longer computation time to reach the exact solution of the problem.
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