LL
L.K. Leunge
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Machine Learning Revealing Insights into Soil Stratification
An Application for Dikes and Dams
Master thesis
(2019)
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Laurens Leunge, Matthijs Kok, Richard Jorissen, Phil Vardon, Bruno Zauda Coelho, Wouter Jan Klerk
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.
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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.