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Levee System Reliability Modeling: The Length Effect and Bayesian Updating

Faculty of Civil Engineering and Geosciences, Delft University of Technology, 42628 Delft, The Netherlands
Deltares, 42628 Delft, The Netherlands
Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, 3052 Melbourne, Australia
Jongejan RMC, 42628 Delft, The Netherlands
TNO, 42628 Delft, The Netherlands
Author to whom correspondence should be addressed.
Received: 12 December 2019 / Revised: 23 January 2020 / Accepted: 25 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue Application of Bayesian Networks to System Safety and Reliability)
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. View Full-Text
Keywords: length effect; system reliability; Bayesian network; proven strength; levee systems; Bayesian updating length effect; system reliability; Bayesian network; proven strength; levee systems; Bayesian updating
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Roscoe, K.; Hanea, A.; Jongejan, R.; Vrouwenvelder, T. Levee System Reliability Modeling: The Length Effect and Bayesian Updating. Safety 2020, 6, 7.

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