Record nummer 2249617
Titel Machine Learning Revealing Insights into Soil Stratification : An Application for Dikes and Dams
Auteur(s) Leunge, L.K.
Uitgever [S.l.] : [s.n.]
Jaar van uitgave 2019
Pagina's 116 p
Online full text
Publicatie type Studentenverslag
Taal Engels
Toelichting (Engels) In the Netherlands, robust dike and dam construction is a major concern and partially dependent on an understanding of subsoil variation. Insights into the subsoil are locally derived for example, by conducting in situ cone penetration tests or boreholes. However, the heterogeneity of the subsoil in the Netherlands, in terms of spatial variation of stratification and soil properties, drastically limits the validity range of a single test. Consequently, large structural designs like dikes and dams have to deal with inevitable spatial subsoil uncertainties. Therefore, technical requirements of dikes and dams have to incorporate a buffer that accounts for these uncertainties. This results in a conservative translation of the safety standard into cross-sectional reliability requirements. Improving the data resolution of a geotechnical analysis would decrease spatial subsoil uncertainties, which in turn would lead to more accurate technical requirements and thereby reduces the construction costs of dikes and dams. This thesis presents a proof of concept of a Machine Learning application, which, by learning locally measured information and analysing high spatial resolution surface settlement data, can increase insights into spatial variation of soil stratification below dikes and dams founded on heterogeneous subsoils, in order to reduce uncertainties regarding spatial variability in cross-sectional reliability requirements.
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