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Calibration of a hypoplastic model using genetic algorithms
Acta Geotechnica ( IF 5.6 ) Pub Date : 2021-02-02 , DOI: 10.1007/s11440-020-01135-z
Francisco José Mendez , Antonio Pasculli , Miguel Alfonso Mendez , Nicola Sciarra

This article proposes an optimization framework, based on genetic algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law, known as Sand Hypoplasticity (SH), allows for robust and accurate modelling of the soil behaviour 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 ExCalibre-Laboratory software from the SoilModels’ website, the GA calibration is tested on a synthetic dataset to analyse 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 and Gudehus, 1999, on the Hochstetten sand. The model parameters identified by the GA optimization improves the matching with the experimental data and hence lead to a better calibration.



中文翻译:

使用遗传算法校准发育不良模型

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的本构定律。这种本构定律称为“沙子低塑性”(SH),可以对土壤行为进行鲁棒而准确的建模,但是需要涉及八个参数的复杂校准。通过将GA与将SH集成在测试条件下的数值求解器组合,拟议的优化可以从测听和三轴排水压缩测试的结果中自动拟合这些参数。通过多次重复相同的校准,优化器的随机特性可以对校准参数进行不确定性量化,并可以研究其在模型预测中的相对重要性。在土壤模型网站上的ExCalibre-Laboratory软件上验证了数值求解器后,将GA校准值放在综合数据集上进行测试,以分析收敛性和结果统计信息。尤其是,相关分析表明,八个模型参数中的两对是高度相关的。最后,在Hochstetten沙滩上,根据von Wolffersdorff(1996年)和Herle和Gudehus(1999年)的结果对校准程序进行测试。通过GA优化确定的模型参数可改善与实验数据的匹配度,从而实现更好的校准。相关分析表明,八个模型参数中的两个是高度相关的。最后,在Hochstetten沙滩上,根据von Wolffersdorff(1996年)和Herle和Gudehus(1999年)的结果对校准程序进行测试。通过GA优化确定的模型参数可改善与实验数据的匹配度,从而实现更好的校准。相关分析表明,八个模型参数中的两个是高度相关的。最后,在Hochstetten沙滩上,根据von Wolffersdorff(1996年)和Herle和Gudehus(1999年)的结果对校准程序进行测试。通过GA优化确定的模型参数可改善与实验数据的匹配度,从而实现更好的校准。

更新日期:2021-02-02
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