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Weighing the importance of model uncertainty against parameter uncertainty in earthquake loss assessments

Abstract : Epistemic uncertainties can be classified into two major categories: parameter and model. While the first one stems from the difficulties in estimating the values of input model parameters, the second comes from the difficulties in selecting the appropriate type of model. Investigating their combined effects and ranking each of them in terms of their influence on the predicted losses can be useful in guiding future investigations. In this context, we propose a strategy relying on variance-based global sensitivity analysis, which is demonstrated using an earthquake loss assessment for Pointe-à-Pitre (Guadeloupe, France). For the considered assumptions, we show: that uncertainty of losses would be greatly reduced if all the models could be unambiguously selected; and that the most influential source of uncertainty (whether of parameter or model type) corresponds to the seismic activity group. Finally, a sampling strategy was proposed to test the influence of the experts' weights on models and on the assumed coefficients of variation of parameter uncertainty. The former influenced the sensitivity measures of the model uncertainties, whereas the latter could completely change the importance rank of the uncertainties associated to the vulnerability assessment step.
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Dernière modification le : vendredi 12 juin 2020 - 16:00:02
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Jeremy Rohmer, John Douglas, Didier Bertil, Daniel Monfort, Olivier Sedan. Weighing the importance of model uncertainty against parameter uncertainty in earthquake loss assessments. Soil Dynamics and Earthquake Engineering, Elsevier, 2014, 58, pp.1-9. ⟨10.1016/j.soildyn.2013.11.006⟩. ⟨hal-00924122⟩

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