In the assessment of existing structures, it is uncommon to consider a track record of the structural performance of the structure itself or similar structures. However, the structure's proven strength in service could play a significant role, along with the performance of similar structures in the population. Because the population track record does not apply in the design of new structures, it is not encountered in design standards. An assessment that does not incorporate the track record may conclude insufficient structural reliability whilst, in reality, the reliability is satisfactory. In the suggested approach, information obtained from laboratory experiments is combined with the track record in a Bayesian way to assess a structure's reliability. As a case study for this article, the reliability of the connection strength between wide slab floor elements is considered. Although laboratory tests indicate poor connection strength, the track record indicates just one failure and many well-performing floors. It is found that considering the time-dependent nature of structural reliability is vital for understanding how proven strength develops from the completion of the structure to its usage today. The number of similar objects in the population that show satisfactory performance is varied and is shown to have a significant effect when its number grows. The presented method and case study show that reliability assessments incorporating a track record enable more accurate structural reliability predictions for existing structures.

In levee system reliability, the length effect is the term given to the phenomenon that the longer the levee, the higher the probability that it will have a weak spot and fail. Quantitatively, it is the ratio of the segment failure probability to the cross-sectional failure probability. The literature is lacking in methods to calculate the length effect in levees, and often over-simplified methods are used. An efficient (but approximate) method, which we refer to as the modified outcrossing (MO) method, was developed for the system reliability model used in Dutch national flood risk analysis and for the provision of levee assessment tools, but it is poorly documented and its accuracy has not been tested. In this paper, we propose a method to calculate the length effect in levees by sampling the joint spatial distribution of the resistance variables using a copula approach, and represented by a Bayesian Network (BN). We use the BN to verify the MO method, which is also described in detail in this paper. We describe how both methods can be used to update failure probabilities of (long) levees using survival observations (i.e., high water levels and no levee failure), which is important because we have such observations in abundance. We compared the methods via a numerical example, and found that the agreement between the segment failure probability estimates was nearly perfect in the prior case, and very good in the posterior case, for segments ranging from 500 m to 6000 m in length. These results provide a strong verification of both methods, either of which provide an attractive alternative to the more simplified approaches often encountered in the literature and in practice.