#Plaxis 2d 2015 hypoplasticity software

I: mathematical foundation and hypoplasticity. Дafalias Y (1986) Bounding surface plasticity. Soils Found 36(1):13вЂ“26Д¬alista M, Pasculli A, Sciarra N (2015) Reconstruction of the geotechnical model considering random parameters distributions.

The model parameters identified by the GA optimization improves the matching with the experimental data and hence lead to a better calibration.Д«auer E (1996) Calibration of a comprehensive hypoplastic model for granular materials. Finally, the calibration procedure is tested on the results from von Wolffersdorff, 1996, and Herle and Gudehus, 1999, on the Hochstetten sand. In particular, a correlation analysis reveals that two couples of the eight model parameters are strongly correlated.

#Plaxis 2d 2015 hypoplasticity software

After validating the numerical solver on the ExCalibre-Laboratory software from the SoilModelsвЂ™ website, the GA calibration is tested on a synthetic dataset to analyse the convergence and the statistics of the results.

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.

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. 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. This article proposes an optimization framework, based on genetic algorithms (GA), to calibrate the constitutive law of von Wolffersdorff.