## 5 Conclusions  
This study demonstrated the usage of modern ensemble learning and data enhancement techniques for data-driven predictions of the structural capacity of columns confined by AFRP material. The datasets have been enhanced using synthetic data points created by generative adversarial networks (GANs). The major points of this paper can be summarized as follows:  
* To predict the ultimate strength and strain of AFRP-wrapped concrete columns, seven different predictive models were developed on datasets of 225 and 159 data points, respectively. The sizes of these datasets have been further increased using data points synthetically generated by generative adversarial networks.
* The machine learning techniques performed significantly better than the predictive equations adopted from the literature. Particularly, the extra trees, XGBoost, and KNN models were the best performing models in the prediction of the ultimate strength and ultimate strain.
* The unconfined ultimate strength of concrete and the elasticity modulus of AFRP were found to have the greatest and least impact on the predicted \(f_{\text{cc}}\) values, respectively. Plots of individual conditional expectations (ICE) and feature dependency were used to illustrate the increasing impact of unconfined ultimate strength on confined ultimate strength. In certain ranges the unconfined ultimate strength was shown to have a decreasing effect on the ultimate strain of the specimens.  
* The ultimate tensile strength of AFRP and the column diameter were found to have the greatest and least impact on the predicted \(\epsilon_{\text{cu}}\) values, respectively.
* The variations of the predicted ultimate strength and ultimate strain values, with respect to the most impactful input features, were visualized using ICE plots.
* It was shown that the equations in the literature can significantly overestimate the compressive strength and ultimate strain of confined concrete.  
This study presented the possibility of increasing the dataset sizes used in predictive model training. Although promising results could be obtained through these ML techniques, the proposed methodology is not recommended to be adopted in practical applications until further validation is undertaken on significantly larger experimental datasets. In particular, in the development of predictive models for the ultimate strain, a reduced dataset has been employed as a base model for the GAN due to the missing values for the ultimate strain values. Future studies in this field should be carried out with larger datasets for both ultimate strength and ultimate strain predictions. Also, predictive models for rectangular columns can be developed in future studies.  
The following supporting information can be downloaded at GitHub link: [[https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)]([https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)) (accessed on 27 October 2023). A dataset of 225 samples with circular cross-sections was collected from the literature [3; 21; 22; 24; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49].  
This research received no external funding.  
The dataset used in this paper is available under the GitHub link [[https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)]([https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)) (accessed on 27 October 2023).  
The author declares no conflict of interest.