Practical machine learning-based prediction model for axial capacity of square CFST columns

Abstract

In this paper, a surrogate Machine-Learning (ML) model based on Gaussian Process Regression (GPR) was developed to predict the axial load of square concrete-filled steel tubular (CFST) columns under compression. For this purpose, an experimental database was extracted from the available literature and used for the development and training of the GPR model. The GPR model’s performance is superior to that of existing models in relation to the axial load of square CFST columns. For practical application, a Graphical User Interface (GUI) was developed for researchers, engineers to support the teaching and interpretation of the axial behavior of CFST columns.

Keywords:

• Square CFST column
• machine learning
• axial behavior
• composite structures

Conflicts of interest

The authors declare no conflict of interest.

Data availability

The raw/processed data required to reproduce these findings will be made available on request.