Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks

Despite its multiple benefits, recycled aggregate concrete (RAC) usually exhibits inferior properties compared with natural aggregate concrete, which has been deemed as a hurdle to its widespread use. One way to overcome this roadblock is to apply FRP jacketing. However, it appears not easy to quantify the axial performance of FRP-confined recycled aggregate concrete columns (FRACC), due largely to the complex load -resisting mechanisms involved. Also, the weakness of RAC itself further compound the problem. This paper aspires to deliver an alternative means to address this difficulty. A powerful boosting approach, XGBoost, was developed to fulfill the goal, where its hyperparameters was fine-tuned by a beetle antennae search metaheuristic algorithm. Meanwhile, a synthetic data generator, tabular generative adversarial networks, was introduced to supplement the limited training data. The model developed outperformed existing empirical equations and several baseline machine learning models. Teng et al.'s FRP-confined concrete model was also improved for better tracing the axial stress-strain behavior. Besides, interpreting the model allows better understanding of the underlying mechanisms such as the minimum reinforcement ratio of FRP required to mitigate the negative effects of RAC. Finally, two data-driven, explicit design equations are given for practical design of FRACC.

Automated summary

This paper presents an alternative approach to evaluating the performance of recycled aggregate concrete columns using FRP jacketing. The XGBoost method and beetle antennae search metaheuristic algorithm are used to optimize the model, and a synthetic data generator is introduced to supplement the training data. The developed model outperforms existing models and improves the understanding of the axial stress-strain behavior of FRP-confined concrete.