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Near‐real‐time loss estimation for instrumented buildings
Author(s) -
Porter Keith,
MitraniReiser Judith,
Beck James L.
Publication year - 2006
Publication title -
the structural design of tall and special buildings
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.895
H-Index - 43
eISSN - 1541-7808
pISSN - 1541-7794
DOI - 10.1002/tal.340
Subject(s) - fragility , probabilistic logic , reliability engineering , component (thermodynamics) , computer science , probability distribution , bayesian probability , structural engineering , overhead (engineering) , structural reliability , structural system , engineering , mathematics , statistics , chemistry , physics , artificial intelligence , thermodynamics , operating system
A technique is developed to model instrumented buildings with a second‐generation performance‐based earthquake engineering approach, producing a damage and loss estimate shortly after the cessation of strong motion. It estimates the likely locations of damage (including concealed structural damage), potentially saving the owner time and money by focusing post‐earthquake safety and repair inspections. It provides a rapid estimate of repair costs, allowing the owner quickly to apply for recovery funds or an insurance claim. The method uses basement accelerograms, a stochastic structural model, and nonlinear time‐history structural analysis to estimate probabilistic engineering demands (structural response). Structural response is input to fragility functions for each damageable assembly to estimate probabilistic physical damage on a component‐by‐component basis. Probabilistic repair costs for each assembly are calculated and summed, and contractor overhead and profit are added to produce a probability distribution of total repair cost. A simple Bayesian‐updating technique employs upper‐story accelerograms to refine the stochastic structural model. It is found in application that using the upper‐story accelerograms produces only a modest change in the distributions of damage and repair cost, because most of the uncertainty in repair cost results from the uncertainty in the fragility functions and cost distributions, not from uncertainty in the structural model. Copyright © 2006 John Wiley & Sons, Ltd.