Advanced predictive machine and deep learning models for round-ended CFST column.

Confined columns, such as round-ended concrete-filled steel tubular (CFST) columns, are integral to modern infrastructure due to their high load-bearing capacity and structural efficiency. The primary objective of this study is to develop accurate, data-driven approaches for predicting the axial load-carrying capacity (P​) of these columns and to benchmark their performance against existing analytical solutions. Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models - LightGBM, XGBoost, and CatBoost - and three deep learning (DL) models - Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM).