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.