Geomaterials such as soil and rocks are highly heterogeneous in both material properties and the geological compositions of the subsurface (see Fig. 1). The impact of material and geospatial uncertainty on dike safety is significant not only because it is extremely difficult to fully characterize the subsurface but also due to the inherent heterogeneity within the dike.
Fig. 1 Geospatial heterogeneity within a dike (source: Geo Risk Management course material)
In this project, you will understand how uncertainty propagates from material properties and geological compositions to the stability of a dike, as well as the inference of material and geospatial uncertainties from in-situ monitoring data. To this aim, a 2D finite element method (FEM) model of a partially saturated dike will be used, together with the data available on DINOloket in order to obtain a realistic measure of soil uncertainty. The results obtained from this statistical-numerical approach will provide new indications on how to incorporate geo-structural uncertainty in practice and how to estimate them from limited data.
The following activities are expected:
- Process geotechnical data from DINOloket to obtain soil characterization parameters on a given site
- Setup a finite element model for a partially saturated dike with given dimensions
- Define boundary conditions such as a varying water table, drying-wetting, or wave overtopping
- Calibrate and perform a sensitivity analysis with the dike model
- Utilize advance sampling techniques for risk assessment of the dike
- Optionally, Bayesian inference/optimization algorithm developed by the supervision team will be used to estimate spatial correlation parameters from limited monitoring data.
This project will give the opportunity to learn finite element modelling, coupled hydro-geomechanical modelling, and stochastic processes. We are looking for an enthusiastic student with a clear interest in (multifunctional) dikes, soil mechanics and numerical modelling. The student should know soil mechanics and random processes and is experienced in MATLAB and/or Python programming.
Supervision
Are you interested in this assignment? Contact the Master thesis coordinator:
