## 1 Introduction
[10] adopted the XGBoost algorithm coupled with Bayesian optimization to develop a hybrid predictive model, namely BO-XGBoost. This model was built considering a database of 320 specimens and demonstrated high performance. In addition, [PERSON] et al. [11] considered six ML algorithms, namely KNN, DT, RF, GBRT, XGBoost, and CatBoost. The predictive models were built considering a database of 457 specimens and 9 input features. The results also indicated that the XGBoost model outperformed all other models. [PERSON] et al. [12] adopted seven ML algorithms, namely LR, ANN, SVR, DT, EoT, XGBoost, and GPR. Then, predictive models for SS of RCDBs were constructed using data from 518 specimens and 15 input parameters. It was demonstrated that GPR is the most accurate model. Moreover, [PERSON] et al. [13] suggested eight ML-based predictive models (e.g., ANN, DT, SVR, RF, GBRT, XGBoost, AdaBoost, and VR) based on a database of 271 samples and 16 input variables. It was revealed that XGBoost outperformed all other models in prediction. Although these ML models have achieved variable levels of accuracy in predicting the SS of RCDBs, there are several shortcomings that still need to be addressed. Firstly, most previous studies were carried out on a limited number of specimens and features, which imposed restrictions on the performance of ML. Hence, it is vital to expand the database to obtain more reliable and robust predictive models. Secondly, some of the above studies [3; 4; 5; 6; 7; 8] were carried out using individual ML algorithms, which lack the generalization ability and model interpretability. Thirdly, there is significant confusion in selecting the effective and appropriate ML algorithms, which is typically the most crucial phase that must be dealt with at the initial stage. Several studies [9; 10; 13] demonstrated that XGBoost performed best, while others [3] indicated that different ML models, like ANN, can provide highly accurate results. On the other hand, the study by [12] showed that GPR is the most accurate model for prediction. This contradiction demands a comprehensive evaluation of various ML models in predicting the SS for RCDBs to provide a shortcut for practicing engineers. Fourthly, only a few studies have examined the model interpretability and transparency related to the shear mechanism of RCDBs. In view of the above, there is a critical need for more comprehensive, accurate, reliable, and practical ML models for predicting the SS of RCDBs. Therefore, the main research objective of this study is to present a novel data-driven predictive approach using an enhanced CatBoost model and incorporating a large database compilation to address the aforementioned shortcomings and concerns. The study further hypothesizes that the CatBoost model can outperform existing models in predicting the SS of RCDBs. Accordingly, the contribution of this study can be clarified as follows: (1) establishing a comprehensive experimental database consisting of 950 shear tests conducted on RCDBs with different web reinforcements. This could further encourage the researchers to adopt the data-driven approach in this field; (2) developing a unified predictive ML model suited for four types of RCDBs and then being evaluated against existing conventional (mechanics-driven) models. This can solve a traditional problem in structural engineering and hence provide an alternative approach for design practices; (3) employing the SHAP and PDPs analyses to investigate the importance and impact of various input features on the CatBoost model prediction in terms of interpretation. The result could assist in designing RCDBs by achieving the desired SS within a few trials, hence saving time, cost, labor, and materials.