Compară metode

	Vulnerability and Damage Function Analysis	Fragility Curve Estimation
Domeniu	Disaster Studies	Disaster Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	2003	2015
Autorul original	Tiziana Rossetto & Amr Elnashai; Charles Kircher, Robert Whitman & William Holmes	Jack W. Baker; Tiziana Rossetto & Amr Elnashai
Tip	Loss-ratio estimation pipeline conditional on hazard intensity	Statistical estimation pipeline for conditional damage probability
Sursa seminală	Rossetto, T., & Elnashai, A. (2003). Derivation of vulnerability functions for European-type RC structures based on observational data. Engineering Structures, 25(10), 1241-1263.	Baker, J. W. (2015). Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis. Earthquake Spectra, 31(1), 579-599.
Denumiri alternative	Damage Function Estimation, Loss Ratio Curves, Mean Damage Ratio Functions, Stage-Damage Functions	Seismic Fragility Functions, Fragility Function Fitting, Conditional Damage Probability Curves, Lognormal Fragility Modeling
Înrudite	4	4
Rezumat	Vulnerability and damage function analysis estimates the expected loss ratio, the repair or replacement cost expressed as a fraction of an asset's value, as a continuous function of hazard intensity. It is the loss-facing counterpart to fragility analysis: where fragility gives the probability of physical damage states, a vulnerability function gives money, translating intensity directly into expected fractional loss together with its uncertainty. Tiziana Rossetto and Amr Elnashai's 2003 derivation of vulnerability functions for European reinforced-concrete buildings from observed damage is a canonical empirical example, while Charles Kircher, Robert Whitman, and William Holmes's 2006 description of HAZUS earthquake methods shows the standard route of combining fragility curves with damage-state loss factors to build them analytically. The output is the per-typology relationship that, multiplied by exposed value, yields scenario and probabilistic loss. Because it bridges engineering damage and economic consequence, it is the single most influential ingredient in catastrophe and loss models. Getting the mean and the spread of the loss ratio right is what makes a risk model usable for insurance, mitigation, and policy.	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.