Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning

In this paper, a new type of combined column, square steel tube hybrid steel–basalt fiber reinforced concrete column (BSFCFST), is proposed for the first time, and a new hybrid machine learning model, NRBO-XGBoost, is proposed to predict the axial compressive load capacity of BSFCFST. Eleven specimens were designed and fabricated to investigate the axial mechanical properties of BSFCFST. The variables considered include basalt fiber volume content, steel fiber volume content, steel tube wall thickness and specimen length to slenderness ratio. The characteristics of damage modes, load-displacement curves and load-strain curves of the new combined columns were mainly investigated. The results showed that the hybrid fibers improved the ultimate load carrying capacity of the specimen, and the improvement of the ductility was obvious. On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. The combined model NRBO-XGBoost was evaluated by comparing it with several prediction methods. The results show that the prediction accuracy of the NRBO-XGBoost model is significantly higher than that of other prediction methods, with R2 = 0.988, which is a good alternative to existing empirical models.