To protect the Netherlands better from flooding, and with an eye on sea-level rise in the rest of the world, more accurate assessments are needed for dykes. The calculation for the most occurring failure mechanism in dykes, i.e. macro-instability, is limited by not being able to calculate large deformation when sliding occurs within a dyke. The random material point method (RMPM) is able to capture the complete failure path, including the residual dyke strength, while taking the heterogeneity of the soil into account, thereby improving the assessment of failure processes. However, as the method is not (yet) used in current practice, clear communication of the results is essential for convincing a wider public of the contribution of this method. Visuals are an important tool in communication, as it increases comprehension of the subject matter if the visual is designed efficiently. This research aims to investigate the available software and which techniques are suitable to make realistic and informative visualizations for a given RMPM dataset of slope failure problems.
A method to create visualization with a certain graphical realism in three-dimensional space is developed. The technique uses a computer graphic software (Blender) combined with an add-on, i.e. an extension plugin. The add-on allows to work with VTK software to process scientific data for visualization, thereby maintaining the scientific correctness of the visualizations. Moreover, a rendering pipeline in Blender is created, which transforms the properties from scientific colors into realistic materials, making the visualizations more intuitive.
However, the dataset is too large to summarize in a straightforward illustration. Therefore, a data analysis is obtained to classify each realization into five pre-defined failure profiles, which are determined based on a literature study. Four failure profiles are classified based on the number of retrogressive failures and whether or not the realization resulted in flooding, while the fifth class describes horizontal failures. A technique has been developed to separate the horizontal failures from the other classes based on the plastic deviatoric strain attribute. Additionally, the data analysis aims to characterize the behavior of each failure profile from an early start, such that the findings could be used for current methods, which could not calculate the full failure profile.
Therefore, this thesis needs to investigate the reduction of the dataset to make it more time efficient when doing a data analysis. It is extended on the clustering algorithm, which has the function to detect failure blocks based on the displacement per dyke profile. The reduction method replaces an amount of data by one representative point per cluster. It not only reduced the size of the dataset significantly, from 3000 GB to 6 GB, it also made the comparison of attributes between realizations, and therefore the data analysis, easier.
The data analysis shows that it is hard to distinguish different failure profiles using only data of the initial failure, which shows the importance of using RMPM to account for post-failure behavior instead of using the current assessment i.e. FEM and LEM. One finding is that equilibrium of the initial failure block is often reached before a vertical crest displacement equal to 0.5 times the height of the dyke. This indicates that the crude estimation in the current assessment is highly conservative. Moreover, within the assumption, it is hypothesized that the secondary failure block will only form after the initial failure block has reached its equilibrium, which is shown otherwise within the data analysis of this thesis.
This work proposes a method for data analysis of RMPM using parallel coordinates, which can be extended to other RMPM datasets for macro-instability and can help to improve the prediction of the probability of flooding. Moreover, it proposes a method to visualize the prominent features, determined using parallel coordinates, in Blender-VTK. This work can, in future research, be extended to other geotechnical problems, such as 3-dimensional dyke slope failure.

Dikes protect people and lands all around the globe. With rising sea levels, the importance of well-designed dikes has never been more essential. One of the main factors that can compromise the stability of dikes are macro-instabilities. Macro-instabilities can cause dikes to lose their water bearing potential, leading to floods. Methods are developed to as accurately as possible determine the probability that a macro-instability takes place in order to prevent it. Therefore, recent advancements include the remaining strength after macro-instability, which may be able to prevent flooding. The foremost methods to determine this remaining strength after macro-instability use D-Stability or the Material Point Method (MPM). Both D-Stability and MPM have advantages and disadvantages. D-Stability can quickly determine the probability that a macro-instability takes place, but cannot model the process of failure, and must therefore simplify this process to estimate the remaining strength. MPM on the other hand can accurately model what happens after a macro-instability, but has a much larger computational cost, especially for probabilistic computations. By supplementing D-Stability and MPM, this thesis proposes a method that exploits the advantages of both methods and mitigates the disadvantages. For a dike with a single clay layer, a connection was made between D-Stability and MPM, allowing dike profiles to be transferred back and forth. In order to make this connection, the SHANSEP undrained shear strength model was successfully implemented in MPM. By using the quick probabilistic D-Stability calculation and the post-failure modeling option of MPM, the probability of failure and the effect of failure can be quickly determined. By taking into account the effect of failure, the method can determine the probability of flooding, without simplifying the failure process. The method can also be used to determine if flooding via retrogressive failure or a larger single instability is more likely. The method was tested via a case study and verified via a RMPM Monte Carlo analysis. For the case study, the probability of flooding was 5.189*10-3 compared to an initial probability of failure of 7.22*10-1, a reduction in the order of 139. This probability of flooding compared well to the probability of flooding of 5.308*10-3computed using the more accurate and computationally expensive RMPM. Based on these first results, the proposed method is a viable method to assess the probability of flooding after macro-instability for clay dikes.