Porovnat metody

	Fragility Curve Estimation	Catastrophe Risk Modeling
Obor	Disaster Studies	Disaster Studies
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2015	2005
Tvůrce	Jack W. Baker; Tiziana Rossetto & Amr Elnashai	Patricia Grossi & Howard Kunreuther; Kirsten Mitchell-Wallace et al.
Typ	Statistical estimation pipeline for conditional damage probability	Event-based stochastic loss-simulation pipeline
Původní zdroj	Baker, J. W. (2015). Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis. Earthquake Spectra, 31(1), 579-599.	Mitchell-Wallace, K., Jones, M., Hillier, J., & Foote, M. (Eds.) (2017). Natural Catastrophe Risk Management and Modelling: A Practitioner's Guide. Wiley-Blackwell. ISBN: 9781118906040
Další názvy	Seismic Fragility Functions, Fragility Function Fitting, Conditional Damage Probability Curves, Lognormal Fragility Modeling	Cat Modeling, Catastrophe Loss Modeling, Natural Catastrophe Modelling, Event-Based Loss Modeling
Příbuzné	4	4
Shrnutí	Fragility curve estimation produces a function that gives the probability that an asset reaches or exceeds a defined damage state as a function of a hazard intensity measure, such as peak ground acceleration or spectral acceleration. It is the central conditional-probability link in disaster risk assessment, sitting between hazard (how strong the shaking is) and loss (what the damage costs), and is almost always parameterized as a lognormal cumulative distribution defined by a median intensity and a logarithmic standard deviation. Tiziana Rossetto and Amr Elnashai's 2003 work derived empirical fragility and vulnerability functions for European reinforced-concrete buildings from large post-earthquake damage databases, while Jack Baker's 2015 paper formalized efficient maximum-likelihood fitting of fragility functions from dynamic structural analyses. The method spans empirical fitting to observed damage, analytical fitting to simulated response, and expert-based judgment when data are scarce. Its output, a small set of curves indexed by damage state, is the reusable vulnerability building block consumed by loss-estimation and catastrophe-modeling pipelines. Estimating these curves well is what makes downstream risk numbers credible rather than arbitrary.	Catastrophe risk modeling estimates the probability distribution of losses from natural perils, such as hurricanes, earthquakes, and floods, by simulating large stochastic sets of plausible events and pushing each through hazard, exposure, vulnerability, and financial modules. It exists because catastrophe losses are rare, severe, and spatially correlated, so historical loss data alone cannot reveal the tail risk that insurers and governments must plan for; instead the model synthesizes thousands of years of possible events. Patricia Grossi and Howard Kunreuther's 2005 volume systematized the four-module structure and its use in managing risk, while Kirsten Mitchell-Wallace and colleagues' 2017 practitioner's guide is the standard modern reference for how the industry builds and uses these models. The defining output is the loss exceedance curve, from which average annual loss, return-period losses, and probable maximum loss are read. Catastrophe models are the engine of property catastrophe insurance, reinsurance pricing, and increasingly public disaster-risk finance. They turn the physics of rare hazards into the financial metrics needed to price and transfer extreme risk.