Evaluating the Proper Damage Indexes of LSF Systems Using NonLinear Time History Analysis and AAN Approach

  • سال انتشار: 1393
  • محل انتشار: دومین کنگره بین المللی سازه ، معماری و توسعه شهری
  • کد COI اختصاصی: ICSAU02_0560
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 1006
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نویسندگان

Hossein Mirza Aghabeik

M.S student of Structure, Department of Civil Engineering, Islamic Azad University, South Tehran Branch,Iran,

Hamid Reza Vosughifar

Associated professor, Department of Civil Engineering, Islamic Azad University, South Tehran Branch, Iran

چکیده

The Lightweight Steel Framing (LSF) system has been proposed as an economical system and earthquake resistant. Due to the lightness of LSF structures, the seismic performance of middle-rise buildings has been improved. Nowadays, various numerical-analytical methods have been proposed for seismic assessment of conventional structures. Providing a perfect seismic damage index is always regarded as one of the analytical passive points. In this research, a LSF building was selected as a case study for Finite Element (FE) modelling in which non-linear time-history analyses have been undertaken. Material properties were defined according to the performed experimental studies. A novel approach was presented for the seismic damage index of LSF systems using the simultaneous incorporation of the non-linear analysis results and the correction coefficient describing the seismic geotechnical effects. The presented seismic damage index indicates the seismic performance of the LSF structures well. Also, a two-layer perceptron Artificial Neural Network (ANN) was trained using the results of the FE model and a non-linear relationship was obtained to predict the seismic damage index. The proposed seismic damage index was finally validated using statistical analyses indicating that the proposed method does not show a significant difference as compared to the ANN results.

کلیدواژه ها

Light Steel Framing, Damage index, Finite element method, Non-linear Time-history analysis, Artificial Neural Network

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