To read this content please select one of the options below:

Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review

Aishwarya Narang (COEDMM, Indian Institute of Technology Roorkee, Roorkee, India)
Ravi Kumar (Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India)
Amit Dhiman (Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, India)

Multidiscipline Modeling in Materials and Structures

ISSN: 1573-6105

Article publication date: 30 December 2022

52

Abstract

Purpose

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Design/methodology/approach

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

Findings

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

Originality/value

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

Keywords

Citation

Narang, A., Kumar, R. and Dhiman, A. (2022), "Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review", Multidiscipline Modeling in Materials and Structures, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MMMS-09-2022-0195

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles