Calibration of the von Wolffersdorff model using Genetic Algorithms

TL;DR

This paper introduces a Genetic Algorithm-based optimization framework for calibrating the complex von Wolffersdorff Sand Hypoplasticity model, enabling automatic parameter fitting, uncertainty quantification, and improved data matching.

Contribution

It presents a novel GA-based calibration method for the von Wolffersdorff model, including uncertainty analysis and validation on synthetic and real data.

Findings

GA effectively calibrates model parameters from test data.

Uncertainty quantification reveals parameter correlations.

Calibration improves fit to experimental data.

Abstract

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.