Porovnat metody

	Fragility Curve Estimation	HAZUS Loss Estimation
Obor	Disaster Studies	Disaster Studies
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2015	2006
Tvůrce	Jack W. Baker; Tiziana Rossetto & Amr Elnashai	Federal Emergency Management Agency; Charles Kircher, Robert Whitman & William Holmes
Typ	Statistical estimation pipeline for conditional damage probability	Standardized GIS-based multi-hazard loss-estimation pipeline
Původní zdroj	Baker, J. W. (2015). Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis. Earthquake Spectra, 31(1), 579-599.	Kircher, C. A., Whitman, R. V., & Holmes, W. T. (2006). HAZUS Earthquake Loss Estimation Methods. Natural Hazards Review, 7(2), 45-59.
Další názvy	Seismic Fragility Functions, Fragility Function Fitting, Conditional Damage Probability Curves, Lognormal Fragility Modeling	Hazus-MH Loss Estimation, FEMA Hazus Methodology, Standardized Regional Loss Estimation, Hazus Earthquake Model
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.	HAZUS loss estimation is FEMA's standardized, GIS-based methodology for estimating the physical, social, and economic consequences of earthquakes, floods, hurricanes, and tsunamis across a region. It chains together four conceptual modules, potential hazard, inventory of the built environment, direct physical damage, and induced and economic losses, so that a consistent national framework can produce comparable loss estimates anywhere in the United States. Charles Kircher, Robert Whitman, and William Holmes's 2006 paper documents the earthquake methodology, including its use of capacity-spectrum demand estimation and lognormal fragility curves, and FEMA's technical manuals specify every default inventory, fragility, and loss parameter. The system is distinguished less by methodological novelty than by standardization: it packages decades of earthquake and flood loss science into reproducible software with vetted defaults. Planners, emergency managers, and policymakers use it for scenario planning, mitigation prioritization, and disaster response. Because its defaults are transparent and documented, HAZUS is both a working tool and a reference implementation of regional loss estimation.