## 4 Conclusions  
Monitoring rock damage induced by cracks is an important stage in geotechnical engineering. AE technique is a useful method for monitoring rock damage and has been used by many researchers. To increase the accuracy of the evaluation and prediction of rock damage, it is necessary to consider various AE parameters, but this work is a tough problem due to the complexity of the relationship between several AE parameters and rock damage. In this study, we proposed a random forest-based prediction model of the quantitative rock damage taking into account combined features between several AE parameters. To fulfill this purpose, 10 granite samples from KAERI in Daejeon were prepared, and a uniaxial compression test under the condition of progressive loading in laboratory scale was conducted to obtain the dataset, including the AE parameters and the degree of rock damage based on crack volumetric strain. To consider combined features between rock damage and various AE parameters, an RF algorithm was employed and compared with SVR. As an additional work, parameter analysis was conducted by means of the SHAP for model interpretability. The conclusions derived from the results can be summarized as follows;  
1. It was confirmed that the relationship between cumulative AE parameters and the degree of damage shows a strong nonlinear tendency by comparing SVRRBF with SVRLinear;  
Figure 14: Averaged absolute values of SHAP. The result showed that the cumulative absolute energy is the highest value among the several AE parameters. Obviously, other parameters also contribute to the prediction of rock damage. These results provide rationality for considering various AE parameters for predicting the degree of rock damage. Figure 13: Force plots of specific data points in (**a**) high damage and (**b**) low damage level. In the case of a data point belonging to the high level of damage (0.99), it was confirmed that the cumulative absolute energy and initiation frequency strongly give a positive contribution to the damage value (Figure 13a). In the opposite case at which the damage value is 0.02, the above two parameters strongly offered a negative contribution to the damage value (Figure 14b). The \(R^{2}\), RMSE, and MAPE of the RF for the testing set was 0.989, 0.032, and 0.014 respectively, which are higher generalization performance than that of \(\text{SVR}_{\text{RBF}}\) and are acceptable results for application in the laboratory scale;
3. In both high and low levels degree of the damage, the cumulative absolute energy and initiation frequency were selected as the main parameters affecting the prediction of the damage. However, care should be taken that the initiation frequency should be considered with several AE parameters since this value is sensitive to the type of AE sensor and the adhesion condition to a rock surface;
4. Other AE parameters such as AE count, amplitude, rise time, and hit also contributed to the prediction of rock damage. These results provide rationality for considering various AE parameters for predicting the degree of rock damage. Although the results of this simulation model cannot be used directly to make predictions in situ due to the scale effect, discontinuity, and attenuation characteristics for in-situ rocks, this study suggests the possibility of extension to in-situ application in underground spaces, as subsequent research. Additionally, it provides information that the RF algorithm is a suitable technique and which parameters should be considered for predicting the degree of damage. In future work, we will extend the research to the engineering scale and consider the attenuation characteristics of rocks for practical application. Conceptualization, H.-L.L. ; methodology, H.-L.L. ; writing--original draft, H.-L.L., J.-S.K., C.-H.H., and D.-K.C. ; writing--review and editing, H.-L.L., J.-S.K., C.-H.H., and D.-K.C. ; All authors have read and agreed to the published version of the manuscript.