دوره 1، شماره 3 - ( 6-1390 )
جلد 1 شماره 3 صفحات 448-433 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rofooei F, Kaveh A, Farahani F. ESTIMATING THE VULNERABILITY OF THE CONCRETE MOMENT RESISTING FRAME STRUCTURES USING ARTIFICIAL NEURAL NETWORKS. IJOCE 2011; 1 (3) :433-448
URL: http://ijoce.iust.ac.ir/article-1-49-fa.html
URL: http://ijoce.iust.ac.ir/article-1-49-fa.html
ESTIMATING THE VULNERABILITY OF THE CONCRETE MOMENT RESISTING FRAME STRUCTURES USING ARTIFICIAL NEURAL NETWORKS. عنوان نشریه. 1390; 1 (3) :433-448
چکیده: (29374 مشاهده)
Heavy economic losses and human casualties caused by destructive earthquakes around the world clearly show the need for a systematic approach for large scale damage detection of various types of existing structures. That could provide the proper means for the decision makers for any rehabilitation plans. The aim of this study is to present an innovative method for investigating the seismic vulnerability of the existing concrete structures with moment resisting frames (MRF). For this purpose, a number of 2-D structural models with varying number of bays and stories are designed based on the previous Iranian seismic design code, Standard 2800 (First Edition). The seismically–induced damages to these structural models are determined by performing extensive nonlinear dynamic analyses under a number of earthquake records. Using the IDARC program for dynamic analyses, the Park and Ang damage index is considered for damage evaluation of the structural models. A database is generated using the level of induced damages versus different parameters such as PGA, the ratio of number of stories to number of bays, the dynamic properties of the structures models such as natural frequencies and earthquakes. Finally, in order to estimate the vulnerability of any typical reinforced MRF concrete structures, a number of artificial neural networks are trained for estimation of the probable seismic damage index.
| بازنشر اطلاعات | |
 | این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
