Data-Driven Modelling of Damage Prediction of Granite Using Acoustic Emission Parameters in Nuclear Waste Repository

• 발행기관: 한국방사성폐기물학회
• 저자명: Hang-Lo Lee Jin-Seop Kim Chang-Ho Hong Ho-Young Jeong Dong-Keun Cho
• 간행물 정보: 『Journal of Nuclear Fuel Cycle and Waste Technology (JNFCWT)』Vol.19 No.1, 75~85쪽, 전체 11쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2021.03.31

국문 초록

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.

키워드

Rock damage Acoustic emission Nuclear waste repository Gradient boosting Support vector regression