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TitreFragility curves for assessing the risks of debris flow hazards
AuteurPrieto, J A; Journeay, J M
SourceCanadian Risk and Hazards Network, 9th annual symposium, symposium abstracts; par CRHNet; 2012 p. 39
Année2012
Séries alt.Secteur des sciences de la Terre, Contribution externe 20120125
ÉditeurCRHNet
RéunionCanadian Risk and Hazards Network Symposium 2012; Vancouver; CA; Octobre 24-26, 2012
Documentlivre
Lang.anglais
Mediapapier; en ligne
Formatspdf
Sujetscoulées de débris; établissement de modèles; géologie des dépôts meubles/géomorphologie
Illustrationscharts; photographs
ProgrammeQuantitative risk assessment, Géoscience pour la sécurité publique
LiensCRHNet 2012 Symposium presentation
LiensHazus Canada
Résumé(disponible en anglais seulement)
Quantitative risk assessment methods for debris flow hazards are increasingly used as the basis for disaster mitigation planning in mountainous terrain. Current methods of risk assessment are based on empirical models that relate the depth of debris flow materials at a specific location to an expected level of proportional economic loss. A more rigorous model based on principles of physical vulnerability has recently been developed that utilizes the overall intensity of a debris flow at a specific location (Intensity = depth x flow velocity2) to estimate the probable state of physical damage to common building types at the point of impact. This study extends the capabilities of the physical vulnerability model by introducing a set of corresponding fragility curves that can be used to estimate the probabilities of exceeding specific states of building damage over a continuous range of debris flow intensities. The debris flow fragility curves framework are consistent with those developed for quantitative risk assessment of earthquake, flood and hurricane hazards, and have the potential to be incorporated into standardized loss estimation methods like HAZUS that are used to support disaster mitigation planning and land use decision making at local and regional scales.
GEOSCAN ID291517