4.6 Article

A shear wall element for nonlinear seismic analysis of super-tall buildings using OpenSees

Journal

FINITE ELEMENTS IN ANALYSIS AND DESIGN
Volume 98, Issue -, Pages 14-25

Publisher

ELSEVIER
DOI: 10.1016/j.finel.2015.01.006

Keywords

Frame-core tube; Nonlinear analysis; OpenSees; Multi-layer element; Super-tall building; Shanghai Tower

Funding

  1. National Key Technology RD Program [2013BAJ08B02]
  2. National Natural Science Foundation of China [51222804, 51378299]
  3. Beijing Natural Science Foundation [8142024]

Ask authors/readers for more resources

Numerical simulation has increasingly become an effective method and powerful tool for performance-based earthquake engineering research. Amongst the existing research efforts, most numerical analyses were conducted using general-purpose commercial software, which to some extent limits in-depth investigations on specific topics with complicated nature. In consequence, this work develops a new shear wall element model and associated material constitutive models based on the open source finite element (FE) code OpenSees, in order to perform nonlinear seismic analyses of high-rise RC frame-core tube structures. A series of shear walls, a 141.8-m frame-core tube building and a super-tall building (the Shanghai Tower, with a height of 632 m) are simulated. The rationality and reliability of the proposed element model and analysis method are validated through comparison with the available experimental data as well as the analytical results of a well validated commercial FE code. The research outcome will assist in providing a useful reference and an effective tool for further numerical analysis of the seismic behavior of tall and super-tall buildings. (C) 2015 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available