Prediction of Nonlinear Time History Deflection of Scallop Domes by Neural Networks

Publish Year: 1390
نوع سند: مقاله کنفرانسی
زبان: English
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NCSS03_016

تاریخ نمایه سازی: 30 تیر 1390

Abstract:

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.

Keywords:

earthquake , nonlinear behaviour , radial basis function , adaptive neuro-fuzzy inference system , neural network

Authors

r Kamyab

۱Department of Civil Engineering, University of Kerman, Kerman, Iran

s Gholizadeh

۲The Iranian Academic Center for Education, Culture and Research, Kerman, Iran

e Salajeghe

۳Department of Civil Engineering, Urmia University, Urmia, Iran

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