Cuttability Assessment of Selected Rocks Through Different Brittleness Values

Dursun, Arif Emre; Gökay, Mehmet Kemal

doi:10.1007/s00603-015-0810-2

Cited by 39 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rock brittleness is an important index value de ning the failure characteristics of rocks depending on loading and unloading situations. Brittleness is vital for rock mechanics such as excavability and drillability associated with cutting and rock mechanics [3,[15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Drilling Rate Index Using Artificial Neural Networks and Regression Analysis

Yetkin,

Özfırat,

Özfırat

et al. 2024

Preprint

View full text Add to dashboard Cite

In underground development gallery and tunnelling operations, the drillability properties of rocks have been studied by researchers in recently. Efficiency in excavation and drilling operations mainly depends on the success of mine production processes. Therefore, it is necessary to know the drillability properties of the formation to be excavated or drilled. This information can be obtained by detailed and costly field experiments. In this study, it is investigated whether the drillability properties of rocks can be determined rapidly and reliably depending on the brittleness index of the rocks. Brittleness index is a coefficient used in many mining designs. There exist a number of equations in literature to compute brittleness index of rocks. In this study, a new equation has been proposed for brittleness index as Bnew= \(\frac{\sqrt{{{}_{c}}^{2}+{{}_{t}}^{2}}}{\sqrt{2}{}_{t} }\). Effectiveness of this equation has been tested using linear and multiple regression models and has been compared with other brittleness equations in literature. In addition to Bnew index, effect of uniaxial compressive strength, tensile strength, three other brittleness equations, shore hardness and density variables are examined on drilling rate index value of rocks. Univariate regression, multiple regression and artificial neural networks are employed to estimate drilling rate index using these variables. Results have shown that using second degree multiple regression models and artificial neural networks drilling rate index can be estimated effectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Drilling Rate Index Using Artificial Neural Networks and Regression Analysis

Yetkin,

Özfırat,

Özfırat

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…During assessing rock brittleness, the brittleness index is crucial for evaluating the risks of rockburst, determining the excavation production of shale gas and oil, assessing the excavation efficiency of the TBM shield tunnelling machines, and so on [33][34][35][36][37][38]. Nowadays, various rock brittleness indexes are proposed by different researchers from many perspectives, such as strength, stress-strain, mineral composition, hardness, inner friction angle, Poisson's ratio, and elastic modulus as shown in Table 1 [1,3,7,[16][17][18][19][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Liu

Zhang

et al. 2021

Lithosphere

View full text Add to dashboard Cite

Brittleness is a crucial parameter of rock mass and the key indicator in rock engineerings, such as rockburst prediction, tunnelling machine borehole drilling, and hydraulic fracturing. To solve the problem of using present brittleness indexes, the existing rock brittleness indexes were firstly summarised in this paper. Then, a brittleness index (BL), which considers the ratio of stress drop rate and stress increase rate and the peak stress, was proposed. This new index has the advantage of simplifying the acquisition of key parameters and avoiding dimensional problems, as well as taking the complete stress-strain curves into account. While applying the BL, the peak strain is used to describe the difficulty of brittle failure before the peak point, and the ratio of stress drop to strain increase can reflect the stress drop rate without dimension problem. In order to verify the applicability of BL, through the PFC2D, the microparameters and confining pressure were changed to model different types of rock numerical specimens and different stress condition. The results show that the BL can well reflect and classify the brittleness characteristics of different rock types and characterise the constraint of confining pressure on rock brittleness. Moreover, the influence of microparameter on macroparameter was studied. In order to further verify the reliability of the brittleness index (BL), this study conducted uniaxial and triaxial compression tests (30 MPa) on marble, sandstone, limestone, and granite under different confining pressure.

show abstract

“…For example, the brittleness of rocks is an important indicator to evaluate the risks of rockburst. For underground engineering, rockburst is the most important issue [1]; in tunnel engineering, the rock brittleness determines the excavation efficiency of the TBM shield tunnelling machine; it also determines the efficiency of shale gas and oil production and has a great impact on the hydraulic fracturing of horizontal wells [2][3][4][5]. In order to reduce or even prevent the adverse B effects of disasters on underground engineering and improve mining efficiency, it is extremely important for scientific research to figure out how to accurately evaluate the brittleness of rocks.…”

Section: Introductionmentioning

confidence: 99%

Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Liu

Wang

Zhang

et al. 2019

Frattura Ed Integrità Strutturale

View full text Add to dashboard Cite

Brittleness plays an important role in the brittle failure process of rocks, and is also one of the important mechanical properties of rocks and a key indicator in rock engineering such as hydraulic fracturing, tunnelling machine borehole drilling and rockburst prediction. Therefore, aiming at the applicability of the brittleness index, this paper summarizes and analyzes the existing brittleness indices based on different experimental methods. Through analysis, it is found that many of the existing methods have their limitations. On the other hand, the brittleness evaluation method based on the stress sstrain curve makes it easier to obtain key parameters and quantify them. Therefore, this paper also adopts this practically widely used method. It proposes a brittleness index based on the post-peak stress drop rate of the rock stress-strain curve and the difficulty of brittle failure, verifies by the traditional triaxial surrounding rock pressure test the accuracy and superiority of B L and further explores the differences between the brittleness indices B 8 , B 11 , B 12 and B L. Finally, the brittleness index B L and B 13 are further contrasted by the existing experimental data.

show abstract

Cuttability Assessment of Selected Rocks Through Different Brittleness Values

Cited by 39 publications

References 36 publications

Estimation of Drilling Rate Index Using Artificial Neural Networks and Regression Analysis

Estimation of Drilling Rate Index Using Artificial Neural Networks and Regression Analysis

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Contact Info

Product

Resources

About