Probabilistic capacity and seismic demand models and fragility estimates for reinforced concrete buildings based on three-dimensional analyses
Xu, Hao
Loading…
Permalink
https://hdl.handle.net/2142/72840
Description
Title
Probabilistic capacity and seismic demand models and fragility estimates for reinforced concrete buildings based on three-dimensional analyses
Author(s)
Xu, Hao
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Gardoni, Paolo
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Capacity model
Seismic demand model
Three-dimensional analysis
Fragility
Bi-axial loading
Strength and stiffness degradation.
Abstract
This thesis presents bivariate fragility estimates for reinforced concrete (RC) buildings accounting for their three-dimensional (3D) response to earthquake ground motions conditioning on spectral accelerations in the two planar directions. The fragility estimates are conducted using the demand and capacity models typically for the 3D responses. The demand models expressed in terms of drift are developed as functions of the spectral accelerations in the two planar directions. The demand prediction is compared in a probabilistic framework with the capacity estimates. The proposed capacity models for five performance levels consider the strength and stiffness degradation under the bi-axial loading. The proposed approach for the fragility estimate considers the uncertainties involved in the spectral acceleration components and capacity variation. The proposed approach is illustrated considering a typical 3-story RC building and results are compared with those from a traditional two-dimensional approach. The results indicate that the two-dimensional approach tends to significantly underestimate the fragility.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.