用遗传算法标定von Wolffersdorff模型
Calibration of the von Wolffersdorff model using Genetic Algorithms
摘要：
本文提出了一种基于遗传算法（GA）的优化框架来标定von Wolffersdorff本构关系。这种本构关系被称为砂亚塑性（SH），允许对土壤行为进行鲁棒和准确的建模，但需要涉及八个参数的复杂校准。建议的优化可以自动拟合这些参数的oedometric和三轴排水压缩试验的结果，通过将GA与数值求解器，集成SH在测试条件下。通过多次重复相同的校准，优化器的随机性使得能够量化校准参数的不确定性，并允许研究它们对模型预测的相对重要性。在SoilModels网站的ExCaliber-Laboratory软件上验证数值求解器后，在合成数据集上测试GA校准，以分析结果的收敛性和统计性。特别是，相关性分析表明，两对八个模型参数是强相关的。最后，校准程序进行了测试，从冯Wolffersdorff，1996年，和Herle和Gudehus，1999年，在Hochstetten砂的结果。通过遗传算法优化辨识的模型参数，提高了与实验数据的匹配性，从而导致更好的校准。
英文摘要：
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.
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