## 5 Conclusions  
Structural engineering applications can be analyzed and processed through structure-property relations that are clearly dependent on the input variables. When analytical models are not at hand, ML and statistical methods may be an effective alternative to describe these dependencies using purely data driven methods, overcoming timely and expensive experimental procedures. This paper employs an extended database of experimental measurements that fully describe the mechanical and physical properties of FRP confined concrete specimens and suggests an ML platform to predict the compressive strength of confined concrete.  
Feature engineering concepts are first explored to prepare the dataset and make it more relevant, informative, and predictive. Correlation tests, in the form of a correlation matrix and a VIF test, have spotted no correlations between input features. Moreover, a partial dependence calculation has been performed. This has shown that the effect of the structural FRP parameters, i.e., the unconfined concrete strength, \(f_{co}\), and the FRP thickness, \(t\), are the primary components affecting mostly the FRP compressive strength. This can be useful to drive future optimization of specimens considered for relevant applications.  
Figure 7: Feature importance for the (**a**) LASSO, (**b**) Ridge, (**c**) RF, and (**d**) GBR algorithms.  
Furthermore, dimensionality reduction techniques such as PCA are also leveraged to minimize the number of independent input features, providing a means of calculation boost. It has been shown that by considering six independent input features derived from PCA, one can replace the original eight-input dataset, achieving 93.4% variance.  
The key element of the proposed model is the ML algorithm. All algorithms presented here have shown that they are capable of providing accurate predictions in a wide-parameter space, even in the presence of noise in the observations, which is due to the experimental measurements. The suggested tree-based ML algorithms investigated on the problem perform well and may be incorporated in similar applications without evidence of overfitting, as also verified using a statistical chi-squared test. More specifically, the RF algorithm has achieved optimal measures of prediction accuracy. As for the implied trees inside the RF structure, the feature importance procedure exploited here has revealed that decisions are mainly driven by the significant parameter \(f_{co}\). The remaining input parameters, such as the number of FRP layers, FRP thickness, FRP ultimate tensile stress, diameter and height of the cylindrical concrete specimens, FRP ultimate tensile strain, and FRP elastic modulus, can be employed for the model refinement.  
Overall, the detailed data science and ML procedure suggested in this paper can aid scientific and engineering applications towards imbuing existing and newly created knowledge into computational models by overcoming the traditional pathway, which often demands either expensive experimental procedures or computationally and hardware-intensive techniques.  
Conceptualization, F.S., C.G.P., T.E.K.; methodology, F.S., T.E.K.; software, F.S.; validation, C.G.P., T.E.K., M.V.; formal analysis, C.G.P.; investigation, M.V., C.G.P.; resources, M.V., C.G.P.; data curation, F.S.; writing--original draft preparation, F.S., C.G.P.; writing--review and editing, T.E.K.; visualization, F.S.; supervision, C.G.P., T.E.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data may be available upon reasonable request from the authors.  
The authors declare no conflict of interest.