This article proposes an optimization framework, based on Genetic Algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law is known as Sand Hypoplasticity (SH), and allows for robust and accurate modeling of the soil behavior but requires a complex calibration involving eight parameters. The proposed optimization can automatically fit these parameters from the results of an oedometric and a triaxial drained compression test, by combining the GA with a numerical solver that integrates the SH in the test conditions. By repeating the same calibration several times, the stochastic nature of the optimizer enables the uncertainty quantification of the calibration parameters and allows studying their relative importance on the model prediction. After validating the numerical solver on the ExCaliber-Laboratory software from the SoilModels' website, the GA calibration is tested on a synthetic dataset to analyze the convergence and the statistics of the results. In particular, a correlation analysis reveals that two couples of the eight model parameters are strongly correlated. Finally, the calibration procedure is tested on the results from von Wolffersdorff, 1996, and Herle & Gudehus, 1999, on the Hochstetten sand. The model parameters identified by the Genetic Algorithm optimization improves the matching with the experimental data and hence lead to a better calibration.

中文翻译：

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使用遗传算法校准 von Wolffersdorff 模型

本文提出了一种基于遗传算法 (GA) 的优化框架，用于校准 von Wolffersdorff 本构律。这种本构法则被称为砂质低塑性 (SH)，它允许对土壤行为进行稳健和准确的建模，但需要涉及八个参数的复杂校准。通过将 GA 与将 SH 集成到测试条件中的数值求解器相结合，所提出的优化可以自动根据固结测量和三轴排水压缩测试的结果拟合这些参数。通过多次重复相同的校准，优化器的随机特性使校准参数的不确定性量化成为可能，并允许研究它们对模型预测的相对重要性。在 SoilModels 网站上的 ExCaliber-Laboratory 软件上验证数值求解器后，将在合成数据集上测试 GA 校准，以分析结果的收敛性和统计数据。特别是，相关性分析表明，八个模型参数中的两对是强相关的。最后，根据 von Wolffersdorff, 1996 和 Herle & Gudehus, 1999 在 Hochstetten 沙地上的结果对校准程序进行了测试。遗传算法优化确定的模型参数改进了与实验数据的匹配，从而导致更好的校准。相关性分析表明，八个模型参数中有两对是强相关的。最后，根据 von Wolffersdorff, 1996 和 Herle & Gudehus, 1999 在 Hochstetten 沙地上的结果对校准程序进行了测试。遗传算法优化确定的模型参数改进了与实验数据的匹配，从而导致更好的校准。相关性分析表明，八个模型参数中有两对是强相关的。最后，根据 von Wolffersdorff, 1996 和 Herle & Gudehus, 1999 在 Hochstetten 沙地上的结果对校准程序进行了测试。遗传算法优化确定的模型参数改进了与实验数据的匹配，从而导致更好的校准。