## 4 Conclusions  
This research established a newly comprehensive database of 950 experimental specimens of RCDBs failed in shear collected from open literature. Based on this database, an interpretable data-driven approach was developed using the CB algorithm for predicting the SS of RCDBs. The theoretical background and implementation procedure were given in  
Figure 15: PDPs of CB model using SHAP.  
detail. The performance of the enhanced CB model was then thoroughly evaluated and compared against three data-driven models developed in this research, as well as three prominent mechanics-driven models through several statistical indicators. Finally, the importance of input features and their impacts on CB model prediction were examined through interpretation from both global and local viewpoints, utilizing the SHAP algorithm with partial dependence plots to provide an in-depth look at the behavior of the black box. Consequently, the following findings are concluded:  
1. The CB model achieved the highest prediction accuracy (\(R^{2}\)) and lowest errors (\(RMSE\), \(MAE\), and \(MAPE\)) compared to three other data-driven models, such as RF, ET, and AB.
2. The CB model significantly outperformed the traditional mechanics-driven models such as ACI 318, CSA A23.3, and EU2 in terms of mean, \(SD\), and \(COV\).
3. SHAP analysis revealed that CB models can efficiently capture the shear mechanism of RCDBs, which showed similar trends with mechanics-driven models. The geometric dimensions and concrete properties are the most influential input features on SS of RCDBs, followed by reinforcement properties.
4. PDPs analysis indicated that SS of RCDBs can be significantly improved by increasing the values of \(b\), \(f_{c}^{\prime}\), and \(\rho_{l}\) up to approximately 300 mm, 80 MPa, and 6%, respectively. In contrast, increasing the value of \(a/d\) up to 2.5 can negatively influence the SS.  
Accordingly, the proposed interpretable data-driven CB model has a high potential to be an alternative approach for design practice. This research is expected to be of interest to researchers and practitioners working at the intersection of structural engineering, data analytics, and intelligent systems. However, there is still room for further advancement and improvement, such as adding new data, using more intelligent feature selection and optimization techniques, as well as adopting new approaches to derive explicit mechanism-related equations. Thus, further investigations are encouraged in this direction for future research.  
Conceptualization, I.S.A., N.A.R. and B.H.A.B.; methodology, I.S.A., N.A.R. and B.H.A.B.; validation, N.A.R. and B.H.A.B.; formal analysis, I.S.A.; investigation, I.S.A.; resources, N.A.R. and B.H.A.B.; writing--original draft preparation, I.S.A.; writing--review and editing, N.A.R.; supervision, N.A.R. and B.H.A.B.; funding acquisition, I.S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used in this study can be made available upon request from the authors. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
ANN Artificial Neural Networks KNN K-Nearest Neighbor LR Linear Regression VR Voting Regressor GPR Gaussian Process Regression SVR Support Vector Regression SVM Support Vector Machine LS-SVR Least Squares-Support Vector Regression LS-SVM Least Squares-Support Vector Machine OSVM-AEW Optimized Support Vector Machine -Adaptive Ensemble Weighting