AI-powered GUI for prediction of axial compression capacity in concrete-filled steel tube columns

In this paper, a novel methodology is developed for the characterization of the capacity of rectangular-shaped concrete-filled steel tubes (CFSTs). In the scientific research field, of particular interest is the behavior of long CFST columns under eccentric compressive load. These conditions promote failure mechanisms involving global member buckling. The developed methodologies are based on machine learning techniques found on artificial neural networks (ANNs). Furthermore, optimization methodologies, employing the grey wolf optimization algorithm and the firefly algorithm, have been attempted. For the training and validation of the models, a database consisting of 1,641 experimental tests collected from literature sources has been prepared, containing long and short specimens as well as specimens with or without load eccentricity. As the vast majority of the available experimental tests involve short specimens, the database has been augmented with 216 3D finite element models (FEMs), featuring increased member slenderness values. The calibration of the FEMs has been performed against experimental tests. The performance of the developed models has been measured through a number of performance indices, and compared with available code procedures. They have been found to provide significant improvements, both for short and long CFST columns, with the ANN model optimized with the firefly algorithm outperforming the others. Furthermore, a graphical user interface (GUI) has been developed which can be readily used to estimate the axial load capacity of CFST columns through the optimal ANN model. The developed GUI is made available as a supplementary material.