Geomechanics and Engineering

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Prediction of the static and dynamic mechanical properties of sedimentary rock using soft computing methods

  • Lawal, Abiodun I. (Department of Energy Resources Engineering, Inha University Yong-Hyun Dong) ;
  • Kwon, Sangki (Department of Energy Resources Engineering, Inha University Yong-Hyun Dong) ;
  • Aladejare, Adeyemi E. (Oulu Mining School, University of Oulu) ;
  • Oniyide, Gafar O. (Department of Mining Engineering, Federal University of Technology)
  • Received : 2021.07.11
  • Accepted : 2021.12.13
  • Published : 2022.02.10
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Abstract

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

Keywords

Acknowledgement

This work was supported by Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019H1D3A1A01102993) and Inha University Research Grant (2021).

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