Machine Learning Revealing Insights into Soil Stratification

Machine Learning Revealing Insights into Soil Stratification: An Application for Dikes and Dams

Leunge, Laurens (TU Delft Civil Engineering and Geosciences)

Kok, Matthijs (graduation committee)
Jorissen, Richard (graduation committee)
Vardon, Phil (graduation committee)
Coelho, Bruno Zauda (mentor)
Klerk, Wouter Jan (mentor)

Delft University of Technology

Civil Engineering

2019-01-30

In the Netherlands, robust dike and dam design is a major concern in the context of flood defence. Due to heterogeneity of the subsoil on which these structures are founded, the validity range of in situ tests decreases drastically. Consequently, large uncertainties regarding spatial variation of soil stratification and soil layer parameters are incorporated in the cross-sectional reliability requirements, resulting in conservative designs. This thesis presents a Machine Learning application, which, by learning locally measured information and analysing high spatial resolution surface settlement data, can provide insights into spatial variation of soil stratification. Through the analysis of these insights, the uncertainties regarding spatial variability in cross-sectional reliability requirements can be reduced, which leads to less conservatism in dike and dam construction.

Machine Learning
Support Vector Machines
Soil Stratification
Dike
Dam

http://resolver.tudelft.nl/uuid:1b91c352-0544-4744-9023-4efbcfd8bdd7

Student theses

master thesis

© 2019 Laurens Leunge