No abstract

No abstract

No abstract

No abstract

No abstract

Knowledge of pore water pressure in an earth dam is crucial for analyzing its mechanical stability. In classical calculations of these pressures, great uncertainty exists regarding the permeability of the materials and the representation of their spatial variability. In this article, a probabilistic analysis of pore water pressures based on field data is performed to represent the permeability with a 2D random field established from statistical and geostatistical analyzes. This random field is introduced in a model based on the Finite Element Method (FEM) and the influence of the spatial variability of permeability on pore water pressure is then studied using Monte-Carlo simulations (MCS).

International audienceLevees are large alignment works on which only imperfect information are usually available. In this particular context, a probability-based model is suggested for evaluating the levee performance, taking into account imperfect data in a probabilistic format. Such a model will implement subjective probabilities that have been elicited by expert assessment to evaluate all criteria and uncertainties thereof. Based on a deterministic model of multicriteria aggregation and using Monte Carlo simulations, this will ultimately help build probability distributions for the performance indicator and for each levee section. The benefit of this model is to determine how much an evaluation may be trusted and to help decide which technical actions should be taken to improve a levee section performance. Résumé: Les digues sont des ouvrages à grands linéaires pour lesquels on ne dispose généralement que dinformations imparfaites. Dans ce contexte, on propose un modèle probabiliste dévaluation de la performance des digues permettant la prise en compte des données imparfaites sous format probabiliste. Ce modèle met en œuvre des probabilités subjectives élicitées par jugement expert pour lévaluation des critères et de leurs incertitudes. Sur la base dun modèle déterministe dagrégation multicritère et au moyen de simulations de Monte Carlo, il permet au final de construire des distributions de probabilité pour lindicateur de performance et pour chaque tronçon de digue. Ce modèle présente les avantages dapprécier la confiance qui peut être accordée à une évaluation et de guider le choix des actions techniques pour améliorer la performance dun tronçon de digue. © 2013 Copyright Taylor and Francis Group, LLC

Abstract. In France, levees remain most of the time badly maintained; these long linear structures show signs of weaknesses on numerous occasions. Only incomplete information is usually available. The general lack of data describing the behavior of the infrastructure during unwanted events led to estimate their safety mainly from expert judgment. Thus the ability of the expert to predict the level of functioning of an infrastructure for a type of hazard and its intensity is crucial. An error of judgment can have very serious consequences and the production of reliable information requires the ability of the expert to report accurately the uncertainties in its estimations, as well as associated confidence. In order to meet this need, our research within Incertu project (French Ministry of Ecology funding) aims to produce relevant scientific approaches and tools for the collection and processing reliable experts ¶ statements or combined with a confidence level in the context of uncertain information and input data.