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This study investigates the structural behaviour of double-skin columns, introducing novel – double-skin double filled tubular (DSDFT) columns, which utilize dual steel tubes and concrete to enhance the load-carrying capacity and ductility beyond conventional double-skin hollow tubular (DSHT) columns, employing a combination of finite element model (FEM) and machine learning (ML) techniques. A total of 48 columns (DSHT+DSDFT) were created to examine the impact of various parameters, such as double tube configurations, the thickness of fibre-reinforced polymer (FRP) layer, type of FRP material, and steel tube diameter, on the load-carrying capacity and ductility of the columns. The results were validated against the experimental findings to ensure their accuracy. Key findings highlight the advantages of the DSDFT configuration. Compared to the DSHT columns, DSDFT exhibited a remarkable 19.54% to 101.21% increase in the load-carrying capacity, demonstrating improved ductility and load-bearing capabilities. Thicker FRP layers enhanced the load-carrying capacity up to 15%, however at the expense of the reduced axial strain. It is also observed that glass fibre-reinforced polymer wrapping displayed 25% superior ultimate axial strain than aramid fibre-reinforced polymer wrapping. Four different ML models were examined to predict the axial load-carrying capacity of the columns, with long short-term memory and bidirectional LSTM models emerging as superior choices exhibiting exceptional predictive capabilities. This interdisciplinary approach offers valuable insights into designing and optimizing confined column systems. It sheds light on both double-tube and single-tube configurations, propelling advancements in structural engineering practices for new constructions and retrofitting. Further, it lays out a blueprint for maximizing the performance of the confined columns under the axial compression.
Ali et al. (Mon,) studied this question.