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.
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.