Semantic Scholar Open Access 2020 18 sitasi

New approximate method to identify soft story caused by infill walls

Azadeh Noorifard M. R. Tabeshpour F. M. Saradj

Abstrak

Abstract When the stiffness of a story is much less than the story above it, a significant portion of lateral displacement of the building is concentrated in this story and soft story is formed. One of the main reasons of soft story in buildings is eliminating or reducing the infill walls in lower stories. This reason is more important than the others because structural engineers usually neglect infill walls in modeling the structure, and on the other hand, architects do not consider the seismic behavior of infill walls during architectural design. In this research, based on the provision of soft story in seismic codes, 2277 macro models with different arrangements of infill walls in the adjacent floors were analyzed. By combining the three linear regression equations derived from the results of analyses, an approximate method was proposed for identifying the soft story. In this method, there is no need to the structural specifications and only by geometric specifications of architectural drawings, the arrangement of infill walls in adjacent stories can be checked. So, it can be used from the final stages of basic architectural design both by architects and structural engineers. The evaluation of this method on the analyzed models showed that 93% of models with soft story were identifiable by this method and in 97% of models, the results were reliable, which indicated high performance of this method.

Topik & Kata Kunci

Penulis (3)

A

Azadeh Noorifard

M

M. R. Tabeshpour

F

F. M. Saradj

Format Sitasi

Noorifard, A., Tabeshpour, M.R., Saradj, F.M. (2020). New approximate method to identify soft story caused by infill walls. https://doi.org/10.1016/j.istruc.2020.01.050

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →
Lihat di Sumber doi.org/10.1016/j.istruc.2020.01.050
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
18×
Sumber Database
Semantic Scholar
DOI
10.1016/j.istruc.2020.01.050
Akses
Open Access ✓