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Development and Utilization of Structural Component Databases for Performance-Based Earthquake Engineering

delete2013-08-01
delete218
PRE
AI
D
Dimitrios G. Lignos *
H
Helmut Krawinkler
DOI:10.1061/(ASCE)ST.1943-541X.0000646delete
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摘要

摘要

En 中文
Performance-based earthquake engineering necessitates the development of reliable nonlinear analysis models that are able to simulate the behavior of structures from the onset of damage through collapse. These models provide engineering demand parameters that are then related with damage measures and describe the damage of a building and its components. To accurately simulate dynamic response up to collapse of structures, it is important to model strength and stiffness deterioration of structural components in addition to effects. These models require the use of large sets of experimental data for calibration of their deterioration parameters. This paper discusses the development of three databases on experimental data of steel W-beams, tubular hollow square steel columns, and RC beams. These databases are used for quantification of important parameters that affect the cyclic moment-rotation relationship at plastic hinge regions in steel and RC components. Emphasis is placed on the prediction of collapse of buildings caused by earthquakes. The utilization and importance of the three databases in the context of performance-based earthquake engineering is demonstrated through a case study of a 4-story steel building. Its seismic performance is successfully assessed through collapse.
Keyword:
Structural members
Databases
Steel beams
Columns
Reinforced concrete
Concrete beams
Earthquake engineering
Deterioration
Structural failures
Structural component databases
Steel beams
Tubular columns
Reinforced concrete beams
Performance-based earthquake engineering
Deterioration models
Material variability
Collapse assessment

期刊

Journal of Structural Engineering 封面图
Journal of Structural Engineering
IF:
3.9
论文数:
4.7K
被引数:
2.8W

机构

S
Stanford University
学者数:
9.6W
论文数: 8.2W
被引数: 17.0W
M
McGill University
学者数:
5.5W
论文数: 4.9W
被引数: 7.0W
引用论文

引用论文

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