AI‐Driven Strength Prediction for Concrete‐Filled Steel Tube Column

This article explores a simplified method to predict the load‐carrying capacity of concrete‐filled steel tubular (CFST) columns and analyzes 13 different types of CFST composite columns. The analysis emphasizes the significance of having sufficient high‐quality experimental data to improve the accuracy of prediction models. Research and codal provisions demonstrate that predicting the compressive strength of CFST composite columns involves essential factors such as geometry and material grade. In this prediction model, the slenderness ratio ( λ ) and nondimensional parameters were introduced to enhance the model’s accuracy. The mathematical model predicts almost 82% exact prediction compared to experimental data. The multiple linear regression (MLR) model for CFST strength prediction provides lower accuracy than the codal provision ANSI/AISC 360‐16 and the developed mathematical model. Also, the artificial neural network (ANN) models predict accurately ( R 2 = 1) the load‐carrying capacity of the column with the Levenberg–Marquardt ANN algorithm. This review highlights the effectiveness of AI‐driven approaches in enhancing predictions of the CFST column strength and provides insights into future research directions for improving computational modeling in structural engineering.