A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks

Karaman, Kadir; Kesimal, Ayhan; Ersoy, Hakan

doi:10.1007/s12517-014-1384-0

Cited by 38 publications

(13 citation statements)

References 33 publications

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“…The obtained results confirmed the superiority of the block punch index over Is 50 in terms of prediction of the BTS. The correlation between TS and Is 50 have been also examined by other studies such as Kahraman and Gunaydin [16], Heidari et al [8], Basu et al [17], Karaman et al [18] and Mahdiyar et al [19]. Sheorey [20] underlined the broadlyacknowledged statement about the correlation of BTS and uniaxial compressive strength (UCS) in rocks, stating that the compressive strength of rock is roughly ten times greater than the BTS of that rock; though, it should be noted that the rock behavior is rather site-specific.…”

Section: Introductionmentioning

confidence: 89%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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Tensile strength (TS) of rock is one of the important properties in design process of construction civil works such as foundations and tunnels. Brazilian tensile strength (BTS) or splitting test is considered as a well-known method in evaluating TS. The present study attempts to propose a novel metaheuristic approach for the indirect measurement of BTS. This new approach is based on the firefly algorithm (FA) for training and optimizing the consequent parameters of the adaptive neuro-fuzzy inference system (ANFIS). The rock samples collected from a tunnel in Malaysia were tested in the laboratory for the purpose of providing a database consisting of totally 80 samples for analysis. Then, the statistical metrics such as coefficient of determination (R 2) were used to examine the acceptability of the proposed ANFIS-FA and ANFIS models. Finally, it was concluded that the ANFIS-FA (with the R 2 of 0.982) can be effectively used as a robust model to predict BTS. INDEX TERMS Tensile strength, brazilian tensile strength, ANFIS, firefly algorithm.

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Section: Introductionmentioning

confidence: 89%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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“…Rock samples from borehole coring were tested in the laboratory for the determination of engineering properties using the uniaxial compressive strength, Brazilian tensile strength, and point load strength index as well as direct shear strength using suggested methods by [2], [3], [4] & [5]. The rock density and water content was also determined in the laboratory.…”

Section: Methodsmentioning

confidence: 99%

Determination of Segari Rock Slope Excavation Technique using Geological Strength Index (GSI)

Roslan¹,

Omar²,

Baharuddin³

et al. 2018

IJET

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Segari - Ayer Tawar rock materials were generally characterized as slightly weathered (Grade II) to moderately weathered (Grade III). Laboratory tests such as Brazilian tensile strength and point load strength index including direct shear strength were carried out using collected weathering sample from borehole to assess the rock strength. Hence, index testing was used to predict geological strength index, rock failure criterion from Hoek-Brown and deformation modulus mainly for the classification of rock mass engineering properties. The relationship between the uniaxial compressive strength and geological strength index of rocks were used in proposing suitable methods for cutting the rock slope.

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“…Some researchers have developed different indirect tests and approaches to estimate UCS for different rocks. Many researchers suggested simpler tests, such as destructive tests like point load and non-destructive ones like ultrasonic wave velocity, Schmidt rebound hammer tests, as indirect alternatives to estimate UCS (Basu and Kamran 2010;Chang et al 2006;Heidari et al 2012;Kahraman 2001;Karaman et al 2015;Katz et al 2000;Salehin 2017;Singh et al 2012;Yagiz 2011;Yasar and Erdogan 2004). On the other side, some researchers have developed correlations between UCS and the other characteristics such as porosity, density, Brazilian tensile strength, and block punch strength index or with more than one of the mentioned indices using mathematical and statistical methods (Baykasoğlu et al 2008;Kahraman and Gunaydin 2009;Minaeian and Ahangari 2013;Mishra and Basu 2012;Sharma and Singh 2008;Sonmez and Tunusluoglu 2008;Tuğrul and Zarif 1999;Ulusay et al 2001;Yesiloglu-Gultekin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III

Moosavi

Mohammadi

2021

Bull Eng Geol Environ

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The present paper has developed a new multivariate linear regression and adaptive neuro-fuzzy inference processing to predict UCS for weathered granite grade III based on simple input data from point load index, Schmidt rebound hardness, and P wave velocity. Data from 85 rock core samples of this granite type have been selected. By using multivariate regression analysis, three models with two independent variables and one model with three independent variables have been developed. Furthermore, another model has been obtained by using a neuro-fuzzy logic analysis. The root means square error, RMSE, coefficient of determination, R 2 , and the mean absolute percentage error, MAPE, were used as the evaluation criteria of the accuracy of the models. The results have indicated that the regression-based and neuro-fuzzy models are effective, but the accuracy of the neurofuzzy model is in good agreement with the realistic data from the direct test.

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A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks

Cited by 38 publications

References 33 publications

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

Determination of Segari Rock Slope Excavation Technique using Geological Strength Index (GSI)

Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III

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