Rock strength assessment based on regression tree technique View Full Text


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Article Info

DATE

2016-01-07

AUTHORS

Maybelle Liang, Edy Tonnizam Mohamad, Roohollah Shirani Faradonbeh, Danial Jahed Armaghani, Saber Ghoraba

ABSTRACT

Uniaxial compressive strength (UCS) is one of the most important parameters for investigation of rock behaviour in civil and mining engineering applications. The direct method to determine UCS is time consuming and expensive in the laboratory. Therefore, indirect estimation of UCS values using other rock index tests is of interest. In this study, extensive laboratory tests including density test, Schmidt hammer test, point load strength test and UCS test were conducted on 106 samples of sandstone which were taken from three sites in Malaysia. Based on the laboratory results, some new equations with acceptable reliability were developed to predict UCS using simple regression analysis. Additionally, results of simple regression analysis show that there is a need to propose UCS predictive models by multiple inputs. Therefore, considering the same laboratory results, multiple regression (MR) and regression tree (RT) models were also performed. To evaluate performance prediction of the developed models, several performance indices, i.e. coefficient of determination (R2), variance account for and root mean squared error were examined. The results indicated that the RT model can predict UCS with higher performance capacity compared to MR technique. R2 values of 0.857 and 0.801 for training and testing datasets, respectively, suggests the superiority of the RT model in predicting UCS, while these values are obtained as 0.754 and 0.770 for MR model, respectively. More... »

PAGES

343-354

References to SciGraph publications

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  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
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  • 2008-02-14. Determination of mechanical properties of rocks using simple methods in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2015-02-18. Prediction of seismic slope stability through combination of particle swarm optimization and neural network in ENGINEERING WITH COMPUTERS
  • 2011-12-02. Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method in ARABIAN JOURNAL OF GEOSCIENCES
  • 2006-03-30. Predicting Uniaxial Compressive Strength by Point Load Test: Significance of Cone Penetration in ROCK MECHANICS AND ROCK ENGINEERING
  • 1997-10. A hierarchical analysis for rock engineering using artificial neural networks in ROCK MECHANICS AND ROCK ENGINEERING
  • 2012-07-29. Correlating P-wave Velocity with the Physico-Mechanical Properties of Different Rocks in PURE AND APPLIED GEOPHYSICS
  • 2015-04-25. Application of two intelligent systems in predicting environmental impacts of quarry blasting in ARABIAN JOURNAL OF GEOSCIENCES
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    96 rock strength assessment
    97 root mean squared error
    98 samples
    99 samples of sandstone
    100 sandstones
    101 simple regression analysis
    102 sites
    103 squared error
    104 strength
    105 strength assessment
    106 strength test
    107 study
    108 superiority
    109 technique
    110 test
    111 testing dataset
    112 time
    113 training
    114 tree model
    115 tree technique
    116 uniaxial compressive strength
    117 values
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