Calibration of the von Wolffersdorff model using Genetic Algorithms
This work addresses the complex calibration challenge in soil mechanics for researchers and engineers, but it is incremental as it applies an existing optimization method to a specific constitutive model.
The authors tackled the problem of calibrating the von Wolffersdorff sand hypoplasticity model, which requires fitting eight parameters, by proposing a genetic algorithm optimization framework that automatically fits these parameters from oedometric and triaxial drained compression tests, resulting in improved matching with experimental data for Hochstetten sand.
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.