Advanced constitutive model parameter determination, optimisation and selection using a database of triaxial tests and machine learning tools

Abstract

Numerical modelling in Geo-Engineering is used to solve complex problems by simulating, analysing, or predicting soil behaviour under certain loading and boundary conditions. The soil behaviour is simulated by constitutive models that describe the relationship between stresses and strains through a mathematical formulation. Model parameters are used to calibrate model behaviour to physical soil behaviour measured during in-situ testing (e.g. CPT) or laboratory testing (e.g. triaxial testing). The selection of model parameters is challenging as it needs to cope with aspects as, constitutive model limitations, laboratory test limitations, sample disturbance, soil heterogeneity and many other. In this paper a database with over 3000 stress-strain paths measured during triaxial tests is used to derive model parameters for the Hardening Soil Small Strain Stiffness model (HS small). A procedure/algorithm has been developed to calibrate model parameters by comparing measured stress-strain paths to a simulated response from a single stress point constitutive driver. Several data analysis techniques, including machine learning tools, have been used to investigate the relationship between soil properties, soil parameters and HS small model parameters. In this paper the developed methodology and the results of the data analysis are presented.