The buckling behavior of columns, as the most influential members regarding the stability of
structures, has been a long-standing field of interest. Moreover, due to the conservative …

This study aims to investigate the prediction of critical buckling load of steel columns using
two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System …

Abstract Artificial Neural Network (ANN) model was developed as a reliable modeling
method for simulating and predicting the ultimate moment capacities of castellated steel …

The main aim of this study is to develop different hybrid artificial intelligence (AI)
approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs …

Accurate prediction of axial compression capacity (ACC) of concrete-filled steel tubular
(CFST) columns is an important issue to maintain the safety levels of related structures and …

In today's world, which has witnessed unprecedented advances in technology and computer
science, artificial intelligence has emerged as a top field captivating global attention. Often …

The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …

Artificial neural network (ANN) as a machine learning (ML) technique has been successfully
applied in engineering applications such as structural dynamics and structural design. It has …

In this paper, load carrying capacity of simply supported castellated steel beams, susceptible
to web-post buckling, is studied. The accuracy of the nonlinear finite element (FE) method to …

This work aims to develop a novel and practical equation for predicting the axial load of
rectangular concrete-filled steel tubular (CFST) columns based on soft computing …