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

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.

[1]  T. N. Singh,et al.  Indirect estimation of compressive and shear strength from simple index tests , 2016, Engineering with Computers.

[2]  S. Kahraman,et al.  A Comparative Evaluation of Indirect Methods to Estimate the Compressive Strength of Rocks , 2005 .

[3]  T. N. Singh,et al.  Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks , 2011, Engineering with Computers.

[4]  Kripamoy Sarkar,et al.  Estimation of strength parameters of rock using artificial neural networks , 2010 .

[5]  D. Mishra,et al.  Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system , 2013 .

[6]  Danial Jahed Armaghani,et al.  Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances , 2015, Engineering with Computers.

[7]  T. Singh,et al.  A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength , 2008 .

[8]  S. Chehreh Chelgani,et al.  Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks , 2010 .

[9]  Ertan Mert,et al.  An Artificial Neural Network Approach to Assess the Weathering Properties of Sancaktepe Granite , 2014, Geotechnical and Geological Engineering.

[10]  Pijush Samui,et al.  Modeling of tensile strength of rocks materials based on support vector machines approaches , 2013 .

[11]  M. Beiki,et al.  Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks , 2013 .

[12]  Danial Jahed Armaghani,et al.  Prediction of the uniaxial compressive strength of sandstone using various modeling techniques , 2016 .

[13]  Allen W. Hatheway,et al.  The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring; 1974–2006 , 2009 .

[14]  Lale Özbakir,et al.  Prediction of compressive and tensile strength of limestone via genetic programming , 2008, Expert Syst. Appl..

[15]  T. Singh,et al.  Prediction of compressive strength and elastic modulus of carbonate rocks , 2016 .

[16]  M. Heidari,et al.  Predicting the Uniaxial Compressive and Tensile Strengths of Gypsum Rock by Point Load Testing , 2012, Rock Mechanics and Rock Engineering.

[17]  Masoud Monjezi,et al.  An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining , 2012, Neural Computing and Applications.

[18]  D. Mishra,et al.  Use of the block punch test to predict the compressive and tensile strengths of rocks , 2012 .

[19]  Akshay Venkatesh,et al.  Correlation Between Point Load Index and Uniaxial Compressive Strength for Different Rock Types , 2012, Rock Mechanics and Rock Engineering.

[20]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[21]  F. Naseri,et al.  Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones , 2015, Arabian Journal of Geosciences.

[22]  Yan-ping Wang,et al.  A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM , 2008 .

[23]  U. Okkan,et al.  Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks , 2013, Environmental Earth Sciences.

[24]  Saffet Yagiz,et al.  Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer , 2009 .

[25]  C. I. Sachpazis.  Correlating Schmidt hardness with compressive strength and Young's modulus of carbonate rocks , 1991 .

[26]  I. Yilmaz,et al.  Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models , 2009 .

[27]  Mustafa Fener,et al.  A preliminary study on the conversion factor used in the prediction of the UCS from the BPI for pyroclastic rocks , 2015, Bulletin of Engineering Geology and the Environment.

[28]  Z. Moradian,et al.  Predicting the Uniaxial Compressive Strength and Static Young's Modulus of Intact Sedimentary Rocks Using the Ultrasonic Test , 2009 .

[29]  Hadi Fattahi.  Application of improved support vector regression model for prediction of deformation modulus of a rock mass , 2016, Engineering with Computers.

[30]  Xiang Hong,et al.  Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings , 2014 .

[31]  Adnan Aydin,et al.  Predicting Uniaxial Compressive Strength by Point Load Test: Significance of Cone Penetration , 2006 .

[32]  Shen Zhenzhong,et al.  Prediction of the strength of concrete radiation shielding based on LS-SVM , 2015 .

[33]  S. Kahraman.  Evaluation of simple methods for assessing the uniaxial compressive strength of rock , 2001 .