###### Abstract  
Monitoring rock damage subjected to cracks is an important stage in underground spaces such as radioactive waste disposal repository, civil tunnel, and mining industries. Acoustic emission (AE) technique is one of the methods 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 required to consider various AE parameters, but this work is a difficult problem due to the complexity of the relationship between several AE parameters and rock damage. The purpose of this study is to propose a machine learning (ML)-based prediction model of the quantitative rock damage taking into account of combined features between several AE parameters. To achieve the goal, 10 granite samples from KAERI (Korea Atomic Energy Research Institute) in Daejeon were prepared, and a uniaxial compression test was conducted. To construct a model, random forest (RF) was employed and compared with support vector regression (SVR). The result showed that the generalization performance of RF is higher than that of SVRRBF. The \(R^{2}\), RMSE, and MAPE of the RF for testing data are 0.989, 0.032, and 0.014, respectively, which are acceptable results for application in laboratory scale. As a complementary work, parameter analysis was conducted by means of the Shapley additive explanations (SHAP) for model interpretability. It was confirmed that the cumulative absolute energy and initiation frequency were selected as the main parameter in both high and low-level degrees of the damage. This study suggests the possibility of extension to in-situ application, 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. rock damage; acoustic emission; random forest; support vector regression; SHAP +
Footnote †: journal: Medical  
(Figure 1). Crack closure stress is defined as the point at which the axial stiffness curve changes from nonlinear to linear or where the crack volumetric strain converges to zero. The crack initiation stress is defined by the onset of stable crack growth and is indicated as the point at which the crack volumetric strain deviates from zero. Therefore, the range between crack closure and crack initiation is considered as the region showing elastic behavior. Meanwhile, the crack coalescence is referred to as the point where large irregularities in the volumetric stiffness occur. The crack damage stress is characterized by unstable growth of crack and associated with the reversed region in the curve of the total volumetric strain [1; 5]. In recent years, the use of acoustic emission (AE) in underground spaces has been increased. AE technique is a kind of nondestructive method to monitor crack growth in a brittle material. When a material is subjected to a progressive load and cracks develop, it would lead to a sudden release of strain energy of the material, which generates an elastic stress wave from cracks of the materials. Many researchers used AE method to associate crack damage of brittle rocks [1; 6; 7]. [PERSON] et al. [8] used an AE event to correlate rock damage growth. They observed the change of the curve trend of cumulative AE count and then characterized crack evolution into four stages. Some researchers tried to correlate crack evolution characteristics with AE count from the uniaxial compression test [1; 9]. [PERSON] et al. [10] evaluated the quantitative damage stress using cumulative AE count. The results showed that the AE cumulative count curve is divided into three stages according to the axial stress level: the slowly increasing stage, the steady stage, and the sharply increasing stage. [PERSON] et al. [11] and [PERSON] et al.