Predicting the buckling Capacity of Steel Cylindrical Shells with Rectangular Stringers under Axial Loading by using Artificial Neural Networks
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 28، شماره: 8
سال انتشار: 1394
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 381
فایل این مقاله در 6 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJE-28-8_007
تاریخ نمایه سازی: 15 آذر 1394
چکیده مقاله:
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).
کلیدواژه ها:
نویسندگان
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