Predicting elastic properties of intact rocks from index tests using multiple regression modelling

Karakus, Murat; Kumral, Mustafa; Kiliç, Özen

doi:10.1016/j.ijrmms.2004.08.005

Cited by 101 publications

(25 citation statements)

References 14 publications

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“…This method can be useful in those cases where complex relations are involved [40]. As the second stage of the regression analyses, a series of multiple regression analyses were conducted.…”

Section: Multivariate Regression Modelmentioning

confidence: 99%

Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Azimian

2016

Acta Geotech.

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Uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. Using standard laboratory tests to determine UCS is a difficult, expensive and time-consuming task. The main purpose of this study is to develop a general model for predicting UCS of limestone samples and to investigate the relationships among UCS, Schmidt hammer rebound and P-wave velocity (V P ). For this reason, some samples of limestone rocks were collected from the southwestern Iran. In order to evaluate a correlation, the measured and predicted values were examined utilizing simple and multivariate regression techniques. In order to check the performance of the proposed equation, coefficient of determination (R 2 ), root-mean-square error, mean absolute percentage error, variance accounts for (VAF %), Akaike Information Criterion and performance index were determined. The results showed that the proposed equation by multivariate regression could be applied effectively to predict UCS from its combinations, i.e., ultrasonic pulse velocity and Schmidt hammer hardness. The results also showed that considering high prediction performance of the models developed, they can be used to perform preliminary stages of rock engineering assessments. It was evident that such prediction studies not only provide some practical tools but also contribute to better understanding of the main controlling index parameters of UCS of rocks.Keywords Limestone Á P-wave velocity (V P ) Á Schmidt hammer rebound (SHR) Á Simple and multivariate regression Á Uniaxial compressive strength

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Section: Multivariate Regression Modelmentioning

confidence: 99%

Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Azimian

2016

Acta Geotech.

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“…Multivariate regression technique is employed to combine more than one parameter which affects a rock property. This method can be useful in those cases where complex relations are involved (Karakus et al 2005). A comprehensive data analysis including multiple linear regression analysis has been applied between the slaking indices and the physical-chemical properties of the rocks using a statistical package (IBM SPSS Statistics 2012).…”

Section: Prediction Of the Slaking Indices Using The Physical-chemicamentioning

confidence: 99%

Assessing the Behavior of Clay-Bearing Rocks Using Static and Dynamic Slaking Indices

et al. 2015

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Clay-bearing rocks are characterized by a wide variation in engineering properties and their resistance to short term weathering due to wetting and drying phenomenon. In this study, a research study was performed on different clay-bearing rocks selected from the Ilam and the Tajarak regions (Iran) to assess suitability of the static and dynamic standard slaking tests. Also in this paper, in order to estimate durability of the clay-bearing rocks, some physicalchemical properties were used in form of multiple linear regression analyses in relation to the static and dynamic slaking indices. The test results from the static and dynamic standard slaking tests showed that the materials retained in the 2 mm mesh drum consists of disintegrated particles varying in diameter from 2 to 25.4 mm. To achieve a more realistic evaluation of the disintegration properties of rock samples after each wetting-drying cycle, a sieve analysis approach is therefore more appropriate. Comparison of the multiple linear regressions between the slaking indices and physical-chemical properties showed that the disintegration ratio could characterize the durability behavior of the rock samples in both the static and dynamic states. Also, the results indicated that the static slaking index (I s ) was more acceptable for predicting the behavior of clay-bearing rocks than the dynamic durability index (I d ).

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“…The null hypothesis H 0 : b j ¼0 is rejected if either of the following equivalent conditions holds [10]: jtjgt a=2;nÀkÀ1 , or prob(t) oa, where prob(t) is the probability that H 0 is true.…”

Section: T-testmentioning

confidence: 99%

Predicting slake durability index of soft sandstone using indirect tests

Moradian

Ghazvinian

Ahmadi

et al. 2010

International Journal of Rock Mechanics and Mining Sciences

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Predicting elastic properties of intact rocks from index tests using multiple regression modelling

Cited by 101 publications

References 14 publications

Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Assessing the Behavior of Clay-Bearing Rocks Using Static and Dynamic Slaking Indices

Predicting slake durability index of soft sandstone using indirect tests

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