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Calibration of a hypoplastic model using genetic algorithms
https://orcid.org/http://orcid.org/0000-0002-3899-1685
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.
Calibration of a hypoplastic model using genetic algorithms
https://orcid.org/http://orcid.org/0000-0002-3899-1685
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.
Calibration of a hypoplastic model using genetic algorithms
https://orcid.org/http://orcid.org/0000-0002-3899-1685
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.
Calibration of a hypoplastic model using genetic algorithms
Acta Geotech.
Mendez, Francisco José (author) / Pasculli, Antonio (author) / Mendez, Miguel Alfonso (author) / Sciarra, Nicola (author)
Acta Geotechnica ; 16 ; 2031-2047
2021-07-01
17 pages
Article (Journal)
Electronic Resource
English
Genetic algorithm optimization , Hypoplasticity model calibration , Nonlinear regression Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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