Advanced constitutive model parameter determination, optimisation and selection using a database of triaxial tests and machine learning tools
M.T. Stals (TU Delft - Civil Engineering & Geosciences)
R. B.J. Brinkgreve – Mentor (TU Delft - Geo-engineering)
K. Siderius – Mentor (Fugro)
Miguel Cabrera – Mentor (TU Delft - Geo-engineering)
G. Rongier – Mentor (TU Delft - Applied Geology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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. This study shows how these model parameters can be determined, optimised and selected by using over 3000 triaxial test results performed on dutch soils (stored in text files) and machine learning tools.