Machine Learning Revealing Insights into Soil Stratification

An Application for Dikes and Dams

Master thesis (2019)

Authors

L.K. Leunge Civil Engineering & Geosciences

Contributors

M. Kok Hydraulic Structures and Flood Risk - CEG (supervisor 2)
Richard E. Jorissen Hydraulic Structures and Flood Risk - CEG (supervisor 2)
P.J. Vardon Geo-engineering - CEG (supervisor 2)
Bruno Zauda Coelho Deltares (supervisor 1)
W.J. Klerk Hydraulic Structures and Flood Risk - CEG (supervisor 1)
Faculty
Civil Engineering & Geosciences
Copyright: © 2019 Laurens Leunge
Machine Learning Dam Support Vector Machines Dike Soil Stratification
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Published Date

30-01-2019

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

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.