## 6 Conclusions  
This paper proposes an intelligent prediction method for coal and rock mass strength based on the integration of bolt drill drilling signals and the AdaBoost algorithm. This method enables the prediction of intermediate coal and rock mass strength during the support process. The following conclusions were drawn from this study:  
1. This article collected data on the on-site reinforcement and support of tunnels and obtained the original operating status data of anchor rod drilling machines, providing a reliable data source for analysis and model training.
2. Using the Pauta criterion to eliminate invalid and abnormal data from the original dataset, we analyzed the relationship between the strength of the surrounding rock and the drilling data from the anchor drilling rig. Ultimately, the model was established to utilize the mean, standard deviation, and peak-to-peak values of pressure (F), torque (T), vibration (A), and rotational speed (N) in the drilling section as input features.
3. By comparing the prediction results of the SVM, decision tree, and linear base learners within the AdaBoost ensemble model, it was found that the SVM achieved the highest R\({}^{2}\) value of 0.972. Additionally, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators exhibited the smallest values, demonstrating that the model possesses superior predictive accuracy for estimating the uniaxial compressive strength of coal and rock masses.
4. By optimizing the hyperparameter in AdaBoost model training, it is also compared with other most advanced models (CNN model, LSTM model, and RF model). The results show that the AdaBoost SVM model is superior to other models in prediction accuracy and stability, which further verifies its superiority in practical application.
5. Comparing the three types of models that classify predicted values into specific rock types, the SVM model, the tree model, and the linear model demonstrate prediction accuracies of 98.8%, 85.4%, and 75.6%, respectively. This indicates that the AdaBoost ensemble algorithm utilizing SVM-based learners achieves the highest prediction accuracy.  
Z.Y.: methodology, writing--original draft, writing--review and editing. H.L.: conceptualization, experimental design, writing, original draft preparation, and visualization. Z.D.: validation, investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52074209.  
Figure 10: Model prediction accuracy. (**a**) Tree; (**b**) linear.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shaanxi Xiaoboadang Mining Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.