Articles | Volume 14, issue 3
Nat. Hazards Earth Syst. Sci., 14, 689–704, 2014
https://doi.org/10.5194/nhess-14-689-2014
Nat. Hazards Earth Syst. Sci., 14, 689–704, 2014
https://doi.org/10.5194/nhess-14-689-2014

Research article 27 Mar 2014

Research article | 27 Mar 2014

A reliability assessment of physical vulnerability of reinforced concrete walls loaded by snow avalanches

P. Favier1,2, D. Bertrand1, N. Eckert2, and M. Naaim2 P. Favier et al.
  • 1INSA de Lyon, Laboratoire de Génie Civil en Ingénierie Environnementale, 34 avenue des Arts, 69621 Villeurbanne, France
  • 2IRSTEA, UR ETNA, 2 rue de la papeterie BP 76, Université Grenoble Alpes, 38402 Saint-Martin-d'Hères Cedex, France

Abstract. Snow avalanches are a threat to many kinds of elements (human beings, communication axes, structures, etc.) in mountain regions. For risk evaluation, the vulnerability assessment of civil engineering structures such as buildings and dwellings exposed to avalanches still needs to be improved. This paper presents an approach to determine the fragility curves associated with reinforced concrete (RC) structures loaded by typical avalanche pressures and provides quantitative results for different geometrical configurations. First, several mechanical limit states of the RC wall are defined using classical engineering approaches (Eurocode 2), and the pressure of structure collapse is calculated from the usual yield line theory. Next, the fragility curve is evaluated as a function of avalanche loading using a Monte Carlo approach, and sensitivity studies (Sobol indices) are conducted to estimate the respective weight of the RC wall model inputs. Finally, fragility curves and relevant indicators such a their mean and fragility range are proposed for the different structure boundary conditions analyzed. The influence of the input distributions on the fragility curves is investigated. This shows the wider fragility range and/or the slight shift in the median that has to be considered when a possible slight change in mean/standard deviation/inter-variable correlation and/or the non-Gaussian nature of the input distributions is accounted for.

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