دوره 1، شماره 3 - ( 6-1390 )
جلد 1 شماره 3 صفحات 432-419 |
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:
Kamyab R, Salajegheh E. PREDICTION OF NONLINEAR TIME HISTORY DEFLECTION OF SCALLOP DOMES BY NEURAL NETWORKS. IJOCE 2011; 1 (3) :419-432
URL: http://ijoce.iust.ac.ir/article-1-48-fa.html
URL: http://ijoce.iust.ac.ir/article-1-48-fa.html
PREDICTION OF NONLINEAR TIME HISTORY DEFLECTION OF SCALLOP DOMES BY NEURAL NETWORKS. عنوان نشریه. 1390; 1 (3) :419-432
چکیده: (24472 مشاهده)
This study deals with predicting nonlinear time history deflection of scallop domes subject to earthquake loading employing neural network technique. Scallop domes have alternate ridged and grooves that radiate from the centre. There are two main types of scallop domes, lattice and continuous, which the latticed type of scallop domes is considered in the present paper. Due to the large number of the structural nodes and elements of scallop domes, nonlinear time history analysis of such structures is time consuming. In this study to reduce the computational burden radial basis function (RBF) neural network is utilized. The type of inputs of neural network models seriously affects the computational performance and accuracy of the network. Two types of input vectors: cross-sectional properties and natural periods of the structures can be employed for neural network training. In this paper the most influential natural periods of the structure are determined by adaptive neuro-fuzzy inference system (ANFIS) and then are used as the input vector of the RBF network. Results of illustrative example demonstrate high performance and computational accuracy of RBF network.
| بازنشر اطلاعات | |
 | این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
