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Geomechanics and Engineering
  Volume 23, Number 1, October10 2020, pages 61-69
DOI: http://dx.doi.org/10.12989/gae.2020.23.1.061
 


Energy analysis-based core drilling method for the prediction of rock uniaxial compressive strength
Wang Qi, Xu Shuo, Gao Hong Ke, Zhang Peng, Jiang Bei and Liu Bo Hong

 
Abstract
    The uniaxial compressive strength (UCS) of rock is a basic parameter in underground engineering design. The disadvantages of this commonly employed laboratory testing method are untimely testing, difficulty in performing core testing of broken rock mass and long and complicated onsite testing processes. Therefore, the development of a fast and simple in situ rock UCS testing method for field use is urgent. In this study, a multi-function digital rock drilling and testing system and a digital core bit dedicated to the system are independently developed and employed in digital drilling tests on rock specimens with different strengths. The energy analysis is performed during rock cutting to estimate the energy consumed by the drill bit to remove a unit volume of rock. Two quantitative relationship models of energy analysis-based core drilling parameters (ECD) and rock UCS (ECD-UCS models) are established in this manuscript by the methods of regression analysis and support vector machine (SVM). The predictive abilities of the two models are comparatively analysed. The results show that the mean value of relative difference between the predicted rock UCS values and the UCS values measured by the laboratory uniaxial compression test in the prediction set are 3.76 MPa and 4.30 MPa, respectively, and the standard deviations are 2.08 MPa and 4.14 MPa, respectively. The regression analysis-based ECD-UCS model has a more stable predictive ability. The energy analysis-based rock drilling method for the prediction of UCS is proposed. This method realized the quick and convenient in situ test of rock UCS.
 
Key Words
    core drilling; energy analysis; drilling parameter; rock; uniaxial compressive strength; relationship model; prediction method
 
Address
Wang Qi, Xu Shuo, Gao Hong Ke, Zhang Peng and Liu Bo Hong: 1.) State Key Laboratory for Geo-mechanics and Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
2.) Research Center of Geotechnical and Structural Engineering, Shandong University, Jinan 250061, China

Jiang Bei: 1.) State Key Laboratory for Geo-mechanics and Deep Underground Engineering,
China University of Mining & Technology (Beijing), Beijing 100083, China
2.) Research Center of Geotechnical and Structural Engineering, Shandong University, Jinan 250061, China
3.) School of Civil Engineering and Architecture, University of Jinan, Jinan 250022, China
 

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