Predicting the buckling Capacity of Steel Cylindrical Shells with Rectangular Stringers under Axial Loading by using Artificial Neural Networks

Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_IJE-28-8_007

تاریخ نمایه سازی: 15 آذر 1394

Abstract:

A parametric study was carried out in order to investigate the buckling capacity of the vertically stiffened cylindrical shells. To this end, ANSYS software was used. Cylindrical steel shells with different yield stresses, diameter-to-thickness ratios (D/t) and number of stiffeners were modeled andtheir buckling capacities calculated by displacement control nonlinear static analysis. Radial basis function (RBF) neural networks were used to predict the buckling capacity of shells. Herein, 70percent of the results of numerical analyses were used to train the neural network and the remainders totest and validate the results. Results of this study showed that RBF neural networks are useful tools to predict the buckling capacity of vertically stiffened cylindrical shells. It was also shown that buckling capacities of stiffened shells exponentially vary by distance of adjacent stiffeners (unstiffened length).

Authors

z Kalantari

Department of Civil Engineering, Qazvin branch, Islamic Azad University, Iran

m.s razzaghi

Department of Civil Engineering, Qazvin branch, Islamic Azad University, Iran