Accurate and reliable estimation of the axial compression capacity can assist engineers
toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are …

Concrete-filled steel tube columns (CFSTCs) are important elements in the construction
sector and predictive analysis of their behavior is essential. Recent works have revealed the …

The paper proposes two hybrid metaheuristic optimization and artificial neural network
(ANN) methods for the close prediction of the ultimate axial compressive capacity of …

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 …

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) …

The study aimed to propose a robust method for predicting the axial compressive capacity
(N u) of circular concrete-filled steel tube (CCFST) columns. For this purpose, a hybrid …

This study aims to estimate the value and quantify the uncertainty of the compressive
strength of circular concrete-filled steel tube (CCFST) columns under eccentric loading using …

This paper aims to develop a practical artificial neural network tool for predicting the axial
compression capacity of circular concrete-filled steel tube columns with ultra-high-strength …

Predicting the mechanical strength of structural elements is a crucial task for the efficient
design of buildings. Considering the shortcomings of experimental and empirical …

Concrete-filled steel tube columns (CFSTCs) are preferred due to enhanced ductility and
energy absorption. The capability of an artificial neural network (ANN) based analytical …