## 7 Model Fusion  
From the above study, it is found that the ML models demonstrate excellent prediction accuracy in predicting the shear-bearing capacity of RCSWs, but each of the above ML models has different prediction accuracies for different data characteristics in the dataset and there is still room for improvement in the CoV of the predicted-to-experimental ratios; for this reason, the stacking approach is employed to combine the various ML models into a stacking learning method to better improve the prediction accuracy and reliability. First the RCSW shear-bearing capacity database (Section 3), and then, several different types of ML models (Section 5), including single, ensemble, and deep learning models, are combined, and each model is trained using a training set, which together build the first layer of the stack. Next, the predictions of each base model are applied to the training set and the validation (or test) set to obtain the predictions. Subsequently, the predictions of all base models are combined into a new feature set, with each column representing the output of a base model. Next, the selected model (LinearRegression) is used as the second layer model and is trained using the feature set and corresponding actual labels generated in the previous step. In the testing phase, the test set is then first predicted using all base models to generate a new feature set, and it is subsequently fed into the trained meta-model to produce the final prediction. Figure 10 shows the predicted values of each specimen obtained by the stacking model in contrast to the test values. Based on the information in Table 9, the stacked model significantly outperforms other ML models in several prediction performance metrics, reflecting its significant improvement in prediction capability. First, the stacking model has an RMSE of 128.2 on the test set, which is significantly more accurate in prediction compared to models such as DT (279.4), KNN (281.8), and AdaBoost (382.1). Second, the MSE of the stacked model in the test set is 1644.4, which is much lower than that of KNN's 79,421.5, showing its superior fitting ability and sensitivity to data variability. In terms of MAE, the stacked model has a test set MAE of 65.8, which is also better than the DT (116.2) and KNN (137.9), indicating its ability to provide more accurate predictions. Further, in terms of the R\({}^{2}\), the stacking model has an almost perfect R\({}^{2}\) of 0.98, showing its good ability to capture variations and trends in the data, while the other models generally have an R\({}^{2}\) of less than 0.95, which is a  
Figure 10: Comparison of calculated and experimental values obtained from stacking model. poorer explanation. Finally, the CoV of the stacking model stays low on both the training and test sets, reflecting its stability (0.013 for the training set, 0.147 for the test set), and compared to models such as DNN (0.244 for the training set, 0.26 for the test set) and AdaBoost (0.541 for the training set, 0.651 for the test set), the stacking model shows better consistency performance. Overall, the stacking model effectively improves the overall prediction accuracy by integrating the advantages of multiple other models and shows stronger flexibility and performance. Meanwhile, Table 9 summarizes the predictive performance metrics of the stacking model proposed in this paper and the ML models of RCSW's shear strength established by other scholars. It should be noted that the \"-\" in the table indicates that the indicator was not considered in the model evaluation. The models have similar R2 for the training and testing data, and the tested RMSE, MSE, MAE, and CoV of each model are close, and the difference is less than 33.4%. Therefore, the prediction performance of each model is relatively close. The main reason for the difference in the predictive performance of these ML models is the different data sets used. For example, the stacking model in this paper is based on 583 RCSW samples, while [PERSON] et al.