###### Abstract
[12] evaluated the degree of rock damage using the b-value originating from seismology, which is calculated from a relation of AE amplitude and AE frequency. Later, [PERSON] and [PERSON] [13] improved the b-value taken into account for a signal attenuation according to AE sensors location. [PERSON] et al. [14] concluded that the b-value reflects the initiation and propagation of cracks in pre-peak stage in the investigation of the AE from different sized rock. [PERSON] [15] performed a uniaxial compression test for granite specimens and confirmed that the rock damage estimated from the cumulative AE energy is similar to the damage for the crack volumetric strain. Subsequently, he extended the scope of application to the in-situ rock in consideration of crack size and wave attenuation [16]. [PERSON] et al. [17] studied the relation between crack  
Figure 1: Typical stress–strain plot for crack growth of rocks ([PERSON] et al. [4]). A crack damage threshold can be divided into four stages using volumetric stiffness and crack volumetric strain: crack closure (\(\sigma_{cc}\)), crack initiation (\(\sigma_{ci}\)), crack coalescence (\(\sigma_{cs}\)), and crack damage (\(\sigma_{cd}\)). development and AE hit for Beishan granite. The result showed that the number of AE hit increased as the stress increased and increased sharply as the stress is close to rock failure. There are some works to evaluate the failure pattern of rock damage based on multi-AE parameters [18; 19]. They classified the rock failure into shear and tensile mode by using AE frequency and RA value which is a multi-AE parameter obtained from dividing the AE rise time by the AE amplitude. [PERSON] et al. [20] introduced damage-related indicators such as AE activity and AE fault total area which are relevant to AE magnitude and AE event for predicting a rock failure precursor. The aforementioned research gives insights into evaluating the rock damage using different AE parameters, such as AE event or hit, amplitude, frequency, energy, count, and rise time. 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 recent years, machine learning (ML) techniques have been utilized to analyze the complex relationship between inputs and targets in the field of rock engineering with AE technique, such as crack recognition [21], crack localization for rocks [22], ultrasonic wave-based damage detection [23], and damage identification using ultrafast wave scattering simulation [24]. However, there is no ML-based predictive model for quantitative damage of rocks. Among the various ML algorithm, ensemble-based random forest (RF) gives different advantages for modeling: (1) it is a kind of ensemble model that combines a variety of single decision trees to improve the predictive power and overcome outliers, (2) it has been validated to have a better predictive power compared with a single ML-based model in the literature [25; 26]. The purpose of this study is to propose an ML-based prediction model of the quantitative rock damage taking into account of combined features between several AE parameters. In the main study, 10 granite samples from KAERI (Korea Atomic Energy Research Institute) in Daejeon are prepared, and a uniaxial compression test under the condition of progressive loading in laboratory scale is executed to obtain the dataset, including the AE parameters and the degree of rock damage based on crack volumetric strain [27]. To consider combined features between rock damage and various AE parameters, ML-based random forest (RF) is employed and compared with support vector regression (SVR). As a complementary work, parameter analysis is conducted by means of the recently published Shapley additive explanations (SHAP) [28] for model interpretability.