Spatial variability and limited measurements often result in low reliability estimates of geotechnical failure modes of dikes (i.e., earthen flood defences). Required dike reinforcements are usually not executed within a few years after inception, which enables efforts to improve reliability estimates by reducing uncertainty. Often decision makers are unclear on whether uncertainty reduction is worth investing, and which (combination of) methods yields the highest Value of Information (VoI). This paper presents a framework to assess the VoI of two uncertainty reduction methods (proof loading and pore pressure monitoring) for a case study of a typical river dike with an insufficiently stable inner slope, using a decision tree. In all cases, a positive VoI was found for at least one strategy consisting of a proof load test, monitoring or both. The optimal strategy of proof loading and monitoring has a VoI of 4.0 M€, being a reduction in total cost of 25% compared to a conventional dike reinforcement. It was also found that sometimes proof loading enhances the VoI of pore pressure monitoring, which demonstrates the benefits of jointly considering different methods in a single decision tree. The decision framework yields insight in total cost and VoI of risk reduction strategies, which enables decision makers to determine where proof loading and/or pore pressure monitoring are efficient, leading to more efficient flood defence asset management.

Dikes and levees play a crucial role in flood protection. The main causes of levee failures are of geotechnical nature. Geotechnical failure modes are also the main contributors to the probability of failure of flood defenses such as levees due to the large uncertainties in ground conditions. Hence, information on ground conditions and soil properties is crucial in safety assessments and retrofitting designs of levees. The experience in practice with designs and risk assessments is that even with a substantial amount of site investigation and laboratory testing the uncertainties in soil properties often remain large. One way to further reduce uncertainties is to include past performance information. The present papers shows how information of survived loads in the past can be incorporated in reliability assessments by means of Bayesian posterior analysis using the same (physics-based) performance models as the prior reliability assessments. The incorporation of survival information leads to reduced uncertainties and probabilities of failure. Furthermore, an approximate approach using fragility curves is proposed, mainly to make problems with computationally expensive performance functions tractable. After briefly recapping Bayesian reliability updating, we provide examples for slope stability of (flood protection) dikes. The examples illustrate that posterior analysis enables us to reduce the large prior uncertainties in soil strength parameters by "eliminating" implausible samples or regions of the joint PDF of the strength properties using the evidence of survived load conditions (i.e. water levels). The results suggest that the effect of updating the probability of failure with survival evidence can be considerable, especially when the uncertainty in the strength properties dominates the reliability. Dominant strength uncertainties are rather typical in geotechnical engineering, as opposed to, for example, structural engineering, implying that using the method is promising also for other geotechnical applications.