Efficient Floor Vibration Analysis of Shear Wall Buildings

Title
Efficient Floor Vibration Analysis of Shear Wall Buildings
Author(s)
강주원김현수[김현수]
Issue Date
201305
Publisher
ARCHITECTURAL INST JAPAN
Citation
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING, v.12, no.1, pp.57 - 64
Abstract
In residential apartment buildings, vibrations induced by a range of sources can be transferred to neighboring residential units through the walls and slabs. For accurate floor vibration analysis of a shear wall type building, it is essential to use a refined finite element model. The accuracy of finite element analysis (FEA) increases with the increasing number of nodes. On the other hand, it is impractical to model an entire building structure with a fine mesh to accurately predict floor vibrations because of the significant number of degrees of freedom (DOFs). Therefore, structural engineers generally use an isolated floor model, however such model has some limitations in accuracy and adaptability. In this study, an efficient analytical method that can accurately predict floor vibrations using the matrix condensation technique was proposed. Because only DOFs associated with translation perpendicular to walls or slabs are employed in the proposed method, more nodes can be used for floor vibration analysis resulting in more accurate analytical results. The modeling technique using super elements and substructures was introduced to reduce the computational time required for the matrix condensation. Numerical analysis of an example structure confirmed that the proposed method can provide more accurate results compared to the conventional finite element model with a considerably reduced computational time.
URI
http://hdl.handle.net/YU.REPOSITORY/25862
ISSN
1346-7581
Appears in Collections:
건축학부 > 건축학부 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE