Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

1. Introduction
2. Theoretical background
    2.1 Quantitative damage
    2.2 Acoustic emission
3. Data-driven techniques
    3.1 Support vector regression
    3.2 Tree-based gradient boosting
4. Data preparation
5. Result and discussion
    5.1 Model optimization
    5.2 Results of the optimum models
    5.3 Importance analysis for cumulative AEparameters
6. Conclusion
REFERENCES

• Hang-Lo Lee(Korea Atomic Energy Research Institute)
• Jin-Seop Kim(Korea Atomic Energy Research Institute) Corresponding Author
• Chang-Ho Hong(Korea Atomic Energy Research Institute)
• Ho-Young Jeong(Pukyong National University)
• Dong-Keun Cho(Korea Atomic Energy Research Institute)