## 5 Model Evaluation
### Ensemble Models  
The ensemble ML models of RF, Bagging, AdaBoost, and Gradient Boosting are used to establish the shear capacity prediction of the RCSW, and the hyperparameters of each model are optimized by a grid search, and the optimized parameter values are shown in Table 4. For RF, the optimized hyperparameters include the number of trees (n_estimators, optimally 100), the maximum depth (max_depth, optimally 30), the minimum number of samples required for node splitting (min_samples_split, optimally 2), and the minimum number of samples in the leaf nodes (min_samples_leaf (optimally 1). The hyperparameters of the Bagging method, on the other hand, include the number of base learners (n_estimators, optimally 10), the percentage of samples per base learner (max_samples, optimally 0.7), and the percentage of features (max_features, optimally 1). The main hyperparameters of the AdaBoost model are the number of base learners (n_estimators, optimally 100) and the learning rate (learning_rate, optimally 0.1). In the Gradient Boosting model, the hyperparameters include the number of base learners (n_estimators, optimally 50), the learning rate (learning_rate, optimally 0.2), the maximum depth (max_depth, optimally 3), and the sample share (subsample, optimally 0.8). Each value of the above hyperparameter optimization was input into the corresponding model, and the predictive performance of the trained models was evaluated, where Figure 6 shows the predicted values of each specimen obtained by each model compared to the test values. Table 5 shows the prediction performance metrics of each of the ensemble ML models built, and in combination with Figure 6, we can obtain the following: Firstly, the RF model performs very well on the training set, with an RMSE of 71.1 and an R\({}^{2}\) as high as 0.99, showing that the model can match the training data quite well. However, the RMSE on the test set rises sharply to 285.4, showing that the model may be overfitting, leading to a decrease in generalization ability. The MSE and MAE on the training and test sets show the same trend, with the test set error significantly larger than the training set. The CoVs are, respectively, 0.085 and 0.179 on the training and test sets, further indicating an increase in prediction variability on the test set. Second, the Bagging model shows similar deficiencies as the RF. While the RMSE for the training set was 83.1 and had an R\({}^{2}\) value of 0.99, the RMSE for the test set increased to 288.2, indicating that the model performed poorly on unknown data. The model's MAE and MSE also show large absolute errors on the test set, and the CoVs are, respectively, 0.099 and 0.206 for the training and test sets, reflecting a more erratic model performance on the new data. The AdaBoost model performs the worst, with an RMSE as high as 241.4 for the training set and 382.1 for the test set, which are both much greater than those other models. Its MAE and MSE are also extremely high, especially on the test set, indicating its lack of predictive ability, and its R\({}^{2}\) values are 0.91 and 0.88, showing weak generalization performance. Meanwhile, the CoVs are, respectively, 0.541