This thesis aims to find the best way to construct a surrogate model for the inner slope stability of a dike and combine this surrogate model with other (machine learning) models to generate conceptual flood defenses, making it possible to optimize the dike design using an interdisciplinary MCA.

The determination of geotechnical parameters from in-situ tests heavily relies on the use of empirical correlations relating field measurements to soil properties. A large number of these correlations can be found across the literature, each of them yielding a different outcome for the parameter value. This results in a high variability of the potential results, which is not necessarily properly accounted for in the current geotechnical engineering practice. A new approach using graphs has recently been proposed by van Berkom [1] to automatically generate multiple parameter values based on a given set of correlations and equations. The present thesis assesses the quality of the estimates obtained from this new graph system and investigates how multiples outcomes can be combined into a unique result. The quality of the parameter’s estimates is appraised both in terms of accuracy or uncertainty, and in terms of validity. The combination of results from multiple methods showed that it is possible to either build confidence or distrust in the combined outcome depending of the consistency of the contributing results. As a result, this framework enabled a quantitative description of the inter-correlations variability. The conceptual framework has been then applied to geotechnical parameters and CPT-based correlations for coarse-grained soils. The range of applicability of the correlations (e.g. soil type, state of consolidation) is a critical factor that actively influences the final outcome of the system. This automatic system demonstrated its ability to produce parameter values relatively close to
the values obtained with the current practices. Further validation is required to assess the overall performance of the system on a broader scale.