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

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

Stals, Michael (TU Delft Civil Engineering & Geosciences; TU Delft Geo-engineering)

Brinkgreve, R.B.J. (mentor)
Siderius, K. (mentor)
Cabrera, M.A. (mentor)
Rongier, G. (mentor)

Delft University of Technology

Geo-Engineering

2023-08-22

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.

triaxial test
parameter determination
constitutive models
machine learning

http://resolver.tudelft.nl/uuid:464af445-928a-4683-993a-0703e274648b

2024-02-22

Student theses

master thesis

© 2023 Michael Stals