PREDICTION OF AXIAL LOAD CAPACITY OF CONCRETE-FILLED STEEL TUBES WITH CIRCULAR SECTIONS UNDER AXIAL LOAD BY USING ARTIFICIAL NEURAL NETWORKS AND RANDOM FOREST METHODS

Prediction of axial load capacity of stub circular concrete-filled steel tube using fuzzy logic

a r t i c l e i n f o Keywords: Concrete-filled steel tube (CFST) Confinement effect Fuzzy logic Modified learning from example (MLFE) Axial load capacity A circular concrete-filled steel tube (CFST) has several advantages compared with the conventional reinforced concrete member or hollow steel tube, since the tri-axial state of compression of the concrete infill increases its strength and str...

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

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 and their buckling capacities were calculated by displacement control nonlinear static analysis.  Radi...

Numerical Analysis of Concrete-Filled Circular Steel Tubes

Buckling Analysis of Cylindrical Grooved Shell under Axial Load Discs

In this paper, buckling of cylindrical grooved shells under axial load has been examined by theoretical and experimental methods. The shell is made of USA/API – X42 5L steel standards. This material is one of the most common materials used in gas, oil and petrochemical industries. The effect of spiral grooves on cylindrical shell was analyzed, and the results obtained from the Abaqus software w...

Determination of Lateral load Capacity of Steel Shear Walls Based on Artificial Neural Network Models

In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...