Design recommendations for stainless steel I-sections under concentrated transverse loading

© 2019 Elsevier Ltd. All rights reserved.Recent investigations have highlighted the need for improved provisions for determining the resistance of stainless steel I-sections under concentrated transverse loading. Such provisions, which reflect the particular characteristics of the material, have been developed and are described herein. A review of the existing European design formulae for members under concentrated transverse loading is firstly presented. Then a series of parametric studies, based on validated finite element models are described covering I-sections with a range of web slenderness values and different stainless steel grades. On the basis of the numerical results, together with existing experimental data, revised design equations are presented and assessed through reliability analysis performed in accordance with Annex D of EN 1990. The new provisions yield enhanced ultimate load predictions and are expected to be included in the next revision of EN 1993-1-4.Peer reviewedFinal Accepted Versio

Parametrisation des nuages et des precipitations envisagees pour le code atmospherique Mercure

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 26165 C, issue : a.1996 n.21 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Etude de la reconstitution des flux de chaleur et de quantite de mouvement a partir des donnees de la campagne ECLAP

Available at INIST (FR), Document Supply Service, under shelf-number : 26165 C, issue : a.1996 n.11 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Comparaison de modeles de dispersion passive sur un episode donne

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 26165 C, issue : a.1996 n.22 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Using Integrated Nested Laplace Approximations for Modelling Spatial Healthcare Utilization

In this work we propose different spatial models to study hospital recruitment, including some potentially explanatory variables, using data from the hospital of Mulhouse a town located in the north-east of France. Interest is on the distribution over geographical units of the number of patients living in this geographical unit. Models considered are within the framework of Bayesian latent Gaussian models. Our response variable is assumed to follow a binomial distribution, with logit link, whose parameters are the population in the geographical unit and the corresponding risk. The structured additive predictor accounts for effects of various covariates in an additive way, including smoothing functions of the covariates (for example a spatial effect). To approximate posterior marginals, which are not available in closed form, we use integrated nested Laplace approximations (INLA), recently proposed for approximate Bayesian inference in latent Gaussian models. INLA has the advantage of giving very accurate approximations and being faster than MCMC methods when the number of hyperparameters does not exceed 6 (as in our case). Model comparison is performed using the Deviance Information Criterion

Presentation and first applications of a dynamic population model for brown trout (Salmo Trutta) : aid to river management

Available from INIST (FR), Document Supply Serviceunder shelf-number : 26165 C, issue : a.1997 n.12 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc