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2025 Vol.35, Issue 6 Preview Page

Original Article

Damage Characteristics of Underground Openings Under Biaxial Compression Using Acoustic Emission and Classification Learning Methods

음향방출특성과 분류학습기법을 활용한 이축압축조건에서의 지하공동 손상 특성 연구

Min-Jun Kim1
,
Jong-Won Lee2*
,
Junhyung Choi3
,
Hanna Kim1
,
Eui-Seob Park4
김 민준1
,
이 종원2*
,
최 준형3
,
김 한나1
,
박 의섭4
1Senior Researcher, Deep Geological Disposal and Energy Storage Research Center, Korea Institute of Geoscience and Mineral Resources
2Postdoctoral Researcher, Deep Geological Disposal and Energy Storage Research Center, Korea Institute of Geoscience and Mineral Resources
3Senior Engineer, Deep Geological Disposal and Energy Storage Research Center, Korea Institute of Geoscience and Mineral Resources
4Head, Deep Geological Disposal and Energy Storage Research Center, Korea Institute of Geoscience and Mineral Resources
1한국지질자원연구원 방폐물심층처분연구센터 선임연구원
2한국지질자원연구원 방폐물심층처분연구센터 박사후연수자
3한국지질자원연구원 방폐물심층처분연구센터 선임기술원
4한국지질자원연구원 방폐물심층처분연구센터 센터장
*Corresponding Author
31 December 2025. pp. 826-842
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Abstract

This study investigates the feasibility of predicting damage levels around underground openings under deep in-situ stress conditions by combining acoustic emission (AE) analysis with classification learning methods under biaxial loading. Biaxial compression tests were conducted on Berea and Idaho sandstone block specimens containing a central circular cavity, while applying different horizontal stress levels. Representative time- and frequency-domain AE parameters, including counts, energy, amplitude, and initiation frequency, were analyzed with respect to the axial loading history. Despite an approximately twofold difference in uniaxial compressive strength between the two sandstones, the evolution patterns of AE parameters were generally similar, and the influence of horizontal stress level on AE responses was found to be limited. This behavior is interpreted as the effect of lateral confinement, which suppresses microcrack opening and propagation and thereby reduces the sensitivity of AE responses to strength and boundary stress conditions under biaxial loading. A dataset comprising 24 input features—15 AE parameters, 8 mechanical properties, and 1 horizontal stress variable—and four damage levels defined by the ratio of axial load to peak load was then constructed for classification learning. Among 23 classification algorithms tested, the bagged trees algorithm achieved the highest prediction accuracy of 93.9%, with more than 80% classification performance for each damage level. These results demonstrate that the combination of AE features and classification learning methods can quantitatively determine damage levels around circular openings under biaxial compression, and they provide a useful basis for AE-based damage assessment systems for deep underground openings.

Keywords
Underground opening
Biaxial compression experiment
Acoustic emission
Classification Learning

본 연구에서는 심부 지하공간에서의 지하공동 안정성 평가를 위해 이축압축조건하 원형 공동을 포함한 사암 시험편을 대상으로 음향방출(acoustic emission, AE) 특성과 분류학습기법을 활용한 손상단계 예측 가능성을 검토하였다. Berea 및 Idaho 사암 블록에 원형 공동을 천공한 후 두가지 횡방향 하중 조건에서 이축압축실험을 수행하였으며, 카운트(counts), 에너지(energy), 최대진폭(amplitude), 초기주파수(initiation frequency) 등 대표적인 시간 및 주파수 영역 AE 인자를 축방향 하중 이력과 연계하여 분석하였다. 그 결과, 두 사암은 약 2배의 일축압축강도 차이를 보였음에도 AE 인자들의 발생 패턴은 유사하게 나타났으며, 횡방향 하중 수준 변화에 따른 차이 또한 제한적인 것으로 확인되었다. 이는 이축압축조건에서 구속압력이 미세균열의 개방과 확장을 억제함으로써 강도 및 횡방향 하중 차이가 AE 인자에 미치는 영향을 감소시킨 결과로 해석된다. 한편, AE 인자 15개, 암반 기본물성 8개, 횡방향 하중조건 1개를 포함한 총 24개의 예측변수와 축하중 비율에 기반한 4단계 손상단계를 응답변수로 구성하여 23종의 분류학습 알고리즘을 비교한 결과, bagged trees 알고리즘이 약 93.9%의 가장 높은 예측 정확도를 보였고 각 손상단계에 대해서도 80% 이상의 분류 성능을 나타냈다. 이러한 결과는 이축압축조건에서 AE 인자와 분류학습기법을 결합할 경우 지하공동 주변의 손상단계를 정량적으로 구분할 수 있음을 보여주며, 향후 심부 지하공동 안정성 평가 및 음향방출 기반 손상 모니터링 시스템 구축을 위한 기초 자료로 활용될 수 있을 것으로 판단된다.

키워드
지하공동
이축압축실험
음향방출
분류학습기법
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Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 35
  • No :6
  • Pages :826-842
  • Received Date : 2025-12-09
  • Revised Date : 2025-12-16
  • Accepted Date : 2025-12-17
  • DOI :https://doi.org/10.7474/TUS.2025.35.6.826
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