Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections,...

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Bibliographic Details
Main Authors: Anton Chepurnenko, Vasilina Turina, Vladimir Akopyan
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:CivilEng
Subjects:
concrete-filled steel tubular columns
load bearing capacity
ultimate equilibrium
artificial neural networks
machine learning
Online Access:https://www.mdpi.com/2673-4109/5/1/8

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