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Research on Rock Strength Prediction Based on Least Squares Support Vector Machine

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Abstract

In order to estimate the strength parameters of rock, as the direct method by conducting rock mechanical tests is time-consuming and expensive, an indirect method based on soft computing technique is proposed. Least squares support vector machine (LS-SVM) is utilized to develop rock uniaxial compressive strength (UCS) and shear strength (SS) prediction models by considering indirect parameters such as rock density, point load strength, P-wave velocity and slake durability index. The results show that according to the rock physical and mechanical parameters of four rock types, empirical relationships based on statistical regression method are rock type specific, only linear relations existed between point load strength and rock strengths are acceptable with high determination coefficients for whole rock types. The LS-SVM models built for rock UCS and SS prediction have greater determination coefficients than the regression models. The prediction values based on LS-SVM prediction models for rock UCS and SS are both extremely close to the measured values, which indicates the applicability of LS-SVM is supported for estimation of strength parameters of rock.

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Acknowledgements

This study was sponsored by the National Natural Science Foundation of China (Grant No. 51574015). The authors would like to thank anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Wen Li & Zhuoying Tan

  2. Key Laboratory of Ministry of Education of China for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing, 100083, China

    Zhuoying Tan

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  1. Wen Li
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  2. Zhuoying Tan
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Correspondence to Zhuoying Tan.

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Li, W., Tan, Z. Research on Rock Strength Prediction Based on Least Squares Support Vector Machine. Geotech Geol Eng 35, 385–393 (2017). https://doi.org/10.1007/s10706-016-0114-7

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  • DOI: https://doi.org/10.1007/s10706-016-0114-7

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