Estimation of Rock Uniaxial Compressive Strength Using Leeb Hardness and Machine Learning Techniques

Se Jin Park; Tae Young Ko

doi:10.7474/TUS.2024.34.6.787

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2024 Vol.34, Issue 6 Preview Page Next Page

Original Article

Estimation of Rock Uniaxial Compressive Strength Using Leeb Hardness and Machine Learning Techniques Leeb 경도와 머신러닝 기법을 통한 암석 일축압축강도 추정

31 December 2024. pp. 787-805

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Abstract

This study developed a model to predict the uniaxial compressive strength (UCS) of various rocks using non-destructive testing methods, identifying a non-linear relationship between Leeb hardness (LH) and UCS. A total of 195 datasets, including 94 experimental data and 101 literature data, were analyzed to evaluate correlations among LH, P-wave velocity (VP), dry density (Density), and UCS. The analysis of the relationship between LH and UCS showed variable correlations by rock type, with igneous rocks showing the highest correlation (R² = 0.74) and metamorphic rocks showing the lowest (R² = 0.12). In comparing prediction methods, machine learning models significantly outperformed both linear (R² = 0.626) and non-linear regression (R² = 0.644) approaches. Among various machine learning models tested, XGBoost demonstrated the highest performance (R² = 0.902, RMSE = 16.282), particularly when combining multiple variables (LH, VP, Density, Rock type). This study suggests that non-destructive testing, combined with machine learning, can offer an efficient and economical approach to predicting UCS in both field and laboratory settings, with potential for enhanced accuracy through the integration of multiple rock properties.

Keywords

Non-destructive testing

Machine learning

Uniaxial Compressive Strength

Leeb hardness

XGBoost

본 연구에서는 비파괴적 시험 방법을 통해 다양한 암석의 일축압축강도(UCS)를 예측하는 모델을 개발하고, Leeb 경도(LH)와 UCS 간의 비선형 관계를 규명하였다. 직접 실험을 통해 수집한 94개와 문헌에서 수집한 101개를 포함하여 총 195개의 데이터셋을 분석하여 LH, P파 속도(VP), 건조밀도(Density)와 UCS 간의 상관관계를 평가하였다. LH와 UCS의 관계 분석 결과, 암석 유형에 따라 상관성의 차이가 나타났으며, 특히 화성암에서 가장 높은 상관관계(R² = 0.74)를, 변성암에서 가장 낮은 상관관계(R² = 0.12)를 보였다. 예측 방법을 비교한 결과, 머신러닝 모델이 선형회귀(R² = 0.626)와 비선형회귀(R² = 0.644) 방법보다 현저히 우수한 성능을 보였다. 다양한 머신러닝 모델 중 XGBoost가 가장 높은 예측 성능(R² = 0.902, RMSE = 16.282)을 보였으며, 특히 여러 변수(LH, VP, Density, Rock type)를 결합했을 때 예측 정확도가 크게 향상되었다. 본 연구는 비파괴적 시험과 머신러닝을 결합하여 현장과 실험실에서 암석 강도를 신속하고 경제적으로 예측할 수 있는 방법론을 제시하며, 다양한 암석 물성의 통합을 통해 예측 정확도를 더욱 향상시킬 수 있는 가능성을 보여준다.

키워드

비파괴시험

머신러닝

일축압축강도

Leeb 경도

XGBoost

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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 : 34
No :6
Pages :787-805
Received Date : 2024-12-05
Revised Date : 2024-12-18
Accepted Date : 2024-12-20
DOI :https://doi.org/10.7474/TUS.2024.34.6.787

Tunnel and Underground SpaceISSN:1225-1275(Print) 2287-1748(Online)한국암반공학회

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