Determination of instantaneous breaking rate by Geological Strength Index, Block Punch Index and power of impact hammer for various rock mass conditions

Aksoy, Cemalettin; Özacar, Vehbi; Demirel, Nuray; Ozer, S.C.; Safak, S.

doi:10.1016/j.tust.2011.02.005

Cited by 26 publications

(6 citation statements)

References 27 publications

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“…Not only that, in sites where the rock is highly weathered, the rocks are harder to prepare as the rock would crumble, resulting in failure to comply with the testing standard size as per ASTM and ISRM Standards. In order to overcome this, Engineers often utilize indirect testing methods to obtain rock strength parameters as they are simpler, cheaper, quicker and easily adaptable to the field [5][6]. One of the popular indirect methods used for this purpose is the Point Load Test (PLT).…”

Section: Problem Backgroundmentioning

confidence: 99%

Correlation between Uniaxial Compressive Strength and Point Load Strength of Penang Island Granites

Mohamad

Hasbollah

Taib

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Uniaxial compressive strength (UCS) is one of the most widely used strength parameters in geotechnical design. The value is obtained through direct testing method, carried out through careful procedures of testing in a laboratory setting. As such, it is relatively more expensive, more tedious and requires longer time to complete. Considering that UCS is highly important, Engineers often utilize indirect methods, namely the Point Load Test, to obtain the Point Load Index Strength (Is(50)). From this test, the results are correlated in order to predict the UCS value. One of the general correlations is provided by ISRM where UCS=20-25 Is(50). However, it is highly contested as it is not universal for all types of rocks. The main focus of this research is to find the correlation of UCS and Is(50) in the Penang Island area. This research would also attempt to classify the granites according to strength. This study showed that the Penang Island Granites are “weak to very strong” granites with UCS values between 24.76 to 156.82MPa. The granites from the North Pluton are classified as “medium strong to very strong” with UCS values between 37.11 to 156.82MPa. The granites from the South Pluton are classified as “weak to very strong” with UCS values between 24.76 to 141.59 MPa. The recommended correlation between UCS and Is(50) are (1) Overall: 8.385 Is(50) + 30.016 (2) North Pluton: 7.93 Is(50)+ 35.606 (3) South Pluton: 9.03Is(50)+20.138.

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Section: Problem Backgroundmentioning

confidence: 99%

Correlation between Uniaxial Compressive Strength and Point Load Strength of Penang Island Granites

Mohamad

Hasbollah

Taib

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…For rock engineering design, it is important to have a reasonable determination of uniaxial compressive strength (UCS) of rocks [1][2][3][4][5][6]. The traditional experiments of UCS have some disadvantages, e.g., these are time consuming, expensive, resulting in only a limited number of UCS assessments being carried out [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A novel model for prediction of uniaxial compressive strength of rocks

Xue¹

2022

Comptes Rendus. Mécanique

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This paper presents an empirical model for predicting the uniaxial compressive strength (UCS) of rocks using gene expression programming (GEP). A total of 44 datasets collected from the literature was used to construct the GEP model. The GEP model developed is evaluated using four conventional regression models and an artificial neural network (ANN) model in terms of three statistical indices. The comparison results confirmed that the proposed GEP model has the lowest root mean square error (RMSE) and the highest coefficient of determination (R 2 ) and correlation coefficient (R) values compared to the four conventional regression models and the ANN model in the literature. It is concluded that the proposed GEP model can be applied to predict the UCS of rocks.

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“…The conventional tests for UCS, however, have some shortcomings in that they are time-consuming and expensive and use restricted sampling. In addition, it may be difficult to conduct sampling and sample preparations for weak rocks [6,7]. Therefore, an alternative method is needed to provide a better estimation of UCS of rocks.…”

Section: Introductionmentioning

confidence: 99%

A hybrid modelling approach for prediction of UCS of rock materials

Xue¹,

Wei²

2020

Comptes Rendus. Mécanique

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It is necessary to make an accurate assessment of uniaxial compressive strength (UCS) for rock mass classification and rock engineering design. However, there are many shortcomings in the conventional tests for UCS of rocks. The aim of this study is to present a hybrid model by integrating the genetic algorithm (GA) into the least-squares support vector machine (LSSVM) to predict the UCS of rock materials. The GA technique was utilized to improve the forecasting accuracy of the proposed LSSVM. To develop the proposed hybrid GA-LSSVM model, four main factors including the block punch index, point load strength, Schmidt rebound hardness and ultrasonic P-wave velocity were considered as input variables, while the UCS of rock materials was the output. A comparison was conducted among the proposed GA-LSSVM, the adaptive neurofuzzy inference system, the fuzzy inference system, the artificial neural network and the statistical method in accordance with three statistical indexes. The results of the comparisons show that the developed GA-LSSVM model has great potential to accurately estimate the UCS of rock materials.

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Determination of instantaneous breaking rate by Geological Strength Index, Block Punch Index and power of impact hammer for various rock mass conditions

Cited by 26 publications

References 27 publications

Correlation between Uniaxial Compressive Strength and Point Load Strength of Penang Island Granites

Correlation between Uniaxial Compressive Strength and Point Load Strength of Penang Island Granites

A novel model for prediction of uniaxial compressive strength of rocks

A hybrid modelling approach for prediction of UCS of rock materials

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