#1 Most Popular article in Applied Soft Computing (Elsevier), a top-tier journal in the field!

This work introduces a hybrid Machine Learning model augmented by a state-of-the-art conditional tabular generative adversarial network (CTGAN) and Optuna to accurately predict the peak and ultimate axial stress-strain responses of Ultra-high-performance concrete (UHPC) confined with either normal-strength steel (NSS) or high-strength steel (HSS).
Accurate prediction of confined UHPC's stress-strain response is critical for reliable modeling and design of UHPC structural elements.

Grateful for the recognition and the continued interest from the research community!