Cerchar Abrasivity Index Research Articles

Effect of petrographic characteristics on abrasive properties of granitic building stones

The abrasive properties of stone that wear away and destroy the cutting blades and drill heads of boring machines increase the cost of construction and mining projects. This study investigated the effects of petrographic characteristics on the abrasiveness of granitic building stone. The composition and abundance of minerals forming each sample were evaluated by preparing microscopic sections of 18 samples, which were then classified as granite, gabbro or hornfels, that are introduced in the building industry as granite building stone. The effect of the abundance of specific minerals was evaluated by calculating the equivalent quartz content for each sample. Cerchar and Laboratoire Central des Ponts et Chausées (LCPC) testing determined the Cerchar abrasivity index (CAI), LCPC abrasivity coefficient (LAC) and LCPC breakability coefficient (LBC) for samples. Linear and direct relationships having a correlation coefficient of greater than 0.82 were determined for LAC–EQC (equivalent quartz content), CAI–EQC and CAI–LAC. No relationship was found having an acceptable value for R for LBC–EQC in the whole samples; however, grouping them produced an experimental relationship at R > 0.88 for LBC–EQC. It was concluded that the experimental relationships for LAC–EQC, CAI–LAC and LBC–EQC increased for groups having the same mineralogical composition. Comparison of the relations in the present study with those proposed by other researchers shows a similar trend of change in the parameters.

Investigation of Various Parameters Effect on Cerchar Abrasivity Index with PFC3D Modeling

The Cerchar test is one of the appropriate and routine tests for determining the rock abrasion; but as for the costs and pin wear measurement errors in laboratory procedures and lack of access to laboratory equipment, using of numerical modeling can lead to use of greater number of samples required during the course of mechanized excavation and reduce the costs and errors in the laboratory test. In this study, the Cerchar abrasivity test was modeled using PFC3D (Particle Flow Code in 3 Dimensions) software. In order to verify the simulation results, Cerchar laboratory test results obtained by Rostami (Rock Mech Rock Eng 47(5):1905–1919, 2014) were compared with the numerical modeling results. In modeling studies, the effects of some parameters such as apply load, test speed, pin hardness and scratching distance on pin wear were investigated. As a conclusion of the study, good agreement between modeling and experimental results was obtained for a given condition. As in the experiment with various loads in both laboratory tests and modeling, with increasing applied load the Cerchar abrasivity index also increased in the experiment with Rockwell hardness HRC (An abbreviation for Rockwell Hardness measured on the C scale. The Rockwell test determines the hardness by measuring the depth of penetration of an indenter under a large load compared to the penetration made by a preload, that on the C scale use from a indenter with 120° cone and 150 kgf load) 42.

Modeling of Micro Deval abrasion loss based on some rock properties

Aggregate is one of the most widely used construction material. The quality of the aggregate is determined using some testing methods. Among these methods, the Micro Deval Abrasion Loss (MDAL) test is commonly used for the determination of the quality and the abrasion resistance of aggregate. The main objective of this study is to develop models for the prediction of MDAL from rock properties, including uniaxial compressive strength, Brazilian tensile strength, point load index, Schmidt rebound hardness, apparent porosity, void ratio Cerchar abrasivity index and Bohme abrasion test are examined. Additionally, the MDAL is modeled using simple regression analysis and multiple linear regression analysis based on the rock properties. The study shows that the MDAL decreases with the increase of uniaxial compressive strength, Brazilian tensile strength, point load index, Schmidt rebound hardness and Cerchar abrasivity index. It is also concluded that the MDAL increases with the increase of apparent porosity, void ratio and Bohme abrasion test. The modeling results show that the models based on Bohme abrasion test and L type Schmidt rebound hardness give the better forecasting performances for the MDAL. More models, including the uniaxial compressive strength, the apparent porosity and Cerchar abrasivity index, are developed for the rapid estimation of the MDAL of the rocks. The developed models were verified by statistical tests. Additionally, it can be stated that the proposed models can be used as a forecasting for aggregate quality.

Correlation between Cerchar abrasivity index, rock properties, and drill bit lifetime

The Cerchar abrasivity index (CAI) is one of the most widely known index method for identification of rock abrasivity. It is a simple and fast testing method providing reliable information on rock abrasiveness. In this study, the relationships between the CAI and some rock properties such as uniaxial compressive strength (UCS), point load strength, Brazilian tensile strength and Schmidt rebound hardness, and equivalent quartz content (EQC) are examined. The relationships between the CAI and drill bit lifetime is also investigated and the type of drill bit wear observed is mentioned. Additionally, the CAI is modeled using simple and multiple linear regression analysis based on the rock properties. Drill bit lifetime is also modeled based on the CAI. The results show that the CAI increases with the increase of the UCS, point load strength, Brazilian tensile strength, L-type and N-type Schmidt rebound hardness, and the EQC. It is concluded that the higher and the lower bit lifetime are obtained for marl and andesitic-basaltic formation, respectively. Moreover, flushing holes, inserted button, button removal, and failures of button on the bits are determined as the type of drill bit wear. The modeling results show that the models based on the UCS and the EQC give the better forecasting performances for the CAI.

Effects of rock water content on CERCHAR Abrasivity Index

Cerchar Abrasivity Index (CAI) has been commonly used to represent rock abrasion for estimation of tool wear in rock excavation applications. This test is a simple and effective measure of rock abrasion but operating parameters can impact its results. This paper focuses on the influence of water saturation on CAI values based on laboratory testing of 33 sedimentary rock units collected from different regions of Pakistan. The tests include CAI measurements as well as engineering rock properties on both dry and saturated rocks. It was found that overall 79% of CERCHAR abrasivity index values measured on saturated rock surfaces “CAIsat” were less than the CERCHAR abrasivity index values measured on dry rock surfaces “CAIdry”. Meanwhile, 52% of CAIsat values showed significant reduction from CAIdry values in the statistical analysis of CAI results with significance level of 15% (α=0.15). Regression analysis was used to correlate CAIsat values with CAIdry, normally measured in CERCHAR testing. The proposed formula can be used to estimate CAIsat from result of laboratory testing where CAI reported is often CAIdry. This could lead into more accurate estimation of tool wear and related adjustments when CAI is used for estimation of tool wear when excavation is done under wet conditions. Multiple regression was also used to establish a predictive model of CAIsat with petrographical, wear indices and geotechnical rock properties of saturated rock samples. In addition, the validity of correlations of CAI with rock properties that are already published was evaluated.

Investigation into effect of scratch length and surface condition on Cerchar abrasivity index

The Cerchar abrasivity index (CAI) obtained from Cerchar abrasivity tests is an indicator of abrasiveness of rocks, and widely used for the estimation of bit/cutting tool life and wear rates in various mining and tunnelling applications. The effect of scratch length on CAI is investigated in this study by use of West apparatus and 6 different steel styluses with Rockwell Hardness of HRC40-42, HRC44-46, HRC48-50, HRC50-52, HRC54-56, and HRC58-60. The tests are carried out on 15 different rock samples (sedimentary, igneous, and metamorphic) on which rough and sawn cut surfaces with scratch lengths varying between 2mm and 20mm with an increment of 2mm, thus resulting in total 27,000 scratches. It is observed that the CAI values between 85% and 93% are reached at the sliding distance of 10mm while about the final CAI value of 99% is reached at 15mm. It is also observed that the CAI values on rough surfaces are about 18% higher than those on sawn cut surfaces. Besides, it is determined that the most suitable surface condition in CAI test is sawn cut surfaces according to the coefficient of variation of CAI values in measurement depending on the stylus hardness and the measurement surface condition.

Predicting performance of impact hammers from rock quality designation and compressive strength properties in various rock masses

Predicting the performance of the impact hammers is one of the major subjects in determining the economics of the underground excavation projects in which they are utilized. Therefore, researchers have been attracted to developing performance prediction models for these machines. Physical and mechanical properties of rocks have been used to estimate the performance of impact hammers over the last few decades. In this study, the instantaneous breaking rate (IBR, m3/h) of an impact hammer used in construction of Levent-Hisarüstü metro tunnel (Istanbul) is recorded in detail. Sixty rock samples are obtained from tunnel route during the excavation of which the machine is employed. Physical and mechanical property tests are performed on the obtained samples. A data set including uniaxial compressive strength (UCS), rock quality designation index (RQD), Brazilian tensile strength (BTS), density (ρ), Schmidt hammer hardness (SHH), Shore scleroscope hardness (SSH), Cerchar abrasivity index (CAI), and IBR is formed. Regression analysis techniques are applied to the created data set in order to develop a performance prediction model. The investigation results in a model that can predict IBR based on UCS, RQD, and the output power of the impact hammer. The proposed model passes both F-test and t-test at 0.95 confidence level. The soundness of the model is successfully tested against two formerly developed models. Covering a wide range of application and requiring only two of the most common and versatile rock properties as input parameters are the other advantages of the suggested model.

Correlation of physico-mechanical properties of granitic rocks with Cerchar Abrasivity Index in Turkey

During the last decades, the rapid developments in mining operations and tunnel construction have lead to a rapid increase in the number of excavation machines. In order to achieve the expected benefits of mechanical excavation machines, these machines should be selected in accordance with the characteristics of rocks. Tool abrasion is an important factor in hard rock tunnelling, mining, and it is highly affected by rock abrasivity. There are several methods to identify the rock abrasivity. One of the commonly used abrasion test in rock is the Cerchar abrasivity index (CAI). Before selection and implementation of excavating machines, physical and mechanical properties of the rocks should be determined. It’s known that, physical-mechanical properties of granitic rocks are generally better than those of many rock types although they cause some difficulties (tool wear, lost time, etc.) in excavation and increase the cutter costs. The purpose of the present study is to determine empirical relationships between CAI and physical-mechanical properties of different granitic rocks using regression method. In this study, some laboratory experiments were conducted on samples collected from granite quarries in different parts of Turkey, particularly from the Marmara Region. Firstly, petrographical, mineralogical and physical-mechanical characteristics of the collected granitic rocks were determined. Then, empirical relationships between these properties and CAI were determined using method of regression analysis. According to the results obtained, a strong correlation is found between CAI value, quartz content and quartz size of the granitic rocks. In addition, the uniaxial compressive strength and indirect tensile strength of the studied granitic rocks increase as CAI increases. Surface roughness, waviness and peak number of granitic rocks lead to an increase in CAI. On the contrary, Bohme abrasion resistance increases while CAI decreases.

Correlation of equivalent quartz content, Slake durability index and Is50 with Cerchar abrasiveness index for different types of rock

Correlation of equivalent quartz content, Slake durability index and Is50 with Cerchar abrasiveness index for different types of rock

Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling

Abrasiveness of rocks plays an important role in the wear of TBM tunnelling. Cerchar Abrasivity Index (CAI) test is the most commonly used method to estimate rock abrasiveness due to its simple, fast test procedure and economic benefits. This study investigated the correlation between CAI and geomechanical properties of igneous and metamorphic rocks. Single and multiple regression analysis were performed and predictive models for the CAI from geomechanical properties were developed. It is found that single parameter alone is not suitable to predict the value of CAI. The result of the multiple regression analysis shows that quartz content is less influencing factor to predict the value of CAI. Also, the correlation between CAI and NTNU’s Cutter Life Index (CLI) was examined and a predictive model for the CLI from CAI and geomechanical properties was suggested. Finally, the influence of CAI on disc cutter life prediction models was investigated and it was found that the variation of CAI has the maximum effect on the predicted disc cutter life in Gehring model.

Estimation of Cerchar abrasivity index of granitic rocks in Turkey by geological properties using regression analysis

The estimation of abrasivity on granitic rocks is very important in mechanical excavators. The mineralogical composition and physico-mechanical properties of the rock affects of abrasivity properties such as the type and size of the mineral grains and strength. Tool wear is an important parameter in hard rock tunnelling and mining applications that are highly affected by rock abrasivity. The estimation of the Cerchar abrasivity index (CAI) from geological properties is useful for excavation costs. For this purpose, CAI, chemical, petrographical, physical and mechanical tests were carried out on 12 granitic rocks taken from different regions of Turkey. The test results were analysed using the method of least squares regression. Multiple regression models having good correlation coefficients were obtained for the granitic rocks. The CAI tests were correlated with chemical compound and a weak relation was determined. The regression analysis was also repeated for petrographical, physical, and mechanical properties. Statistically meaningful correlations were determined between all geological properties and CAI. Then multiple regression analysis was applied to all geological properties to find the more significant relations. Results show that shore scleroscope hardness, water absorption, and the waveness average prove to be the best for estimating CAI of granitic rocks.

Artificial neural network application to predict the sawability performance of large diameter circular saws

To predict the performance of a large diameter circular saw (LDCS) is among the fundamental steps that are required for determining the practicability of stone production. Natural stone processing plants were visited to measure the areal slab production rate (ASPR) of LDCS in different operational conditions. Neural network toolbox in MATLAB is applied in order to develop a model to predict ASPR of LDCS. An artificial neural network is trained with physical and mechanical properties of eleven stones as input parameters and their associated ASPR values as the target. Uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Cerchar abrasivity index (CAI), porosity, and density are the physical and mechanical properties that are used as input parameters. In view of its speed, robustness, and the fact that it is very well renowned compared to the other learning algorithms, the Levenberg–Marquardt propagation algorithm is used to train the network. It is explained in detail that a neural network with the previously mentioned input parameters and only one hidden-layer can successfully estimate ASPR for LDCS. It is noticed that, while the number of neurons is less than eight in the single hidden-layer, the network generalizes better than when the number of neurons increases. However, beyond that point, not only the number of neurons does not have any positive effect on performance of the network, but it may also cause the network to memorize the results instead of generalizing them. It can be declared that using ANN to predict ASPR of LDCS may lead the engineers toward a more reliable design and planning.

A preliminary study on the role of acoustic emission on inferring Cerchar abrasivity index of rocks using artificial neural network

The wear rate of working tools during the cutting and drilling of rocks is closely related to the abrasiveness of those rocks. As the contact area of the tools increases, due to wear, the specific cutting energy will also increase, and that directly affects the overall consumption of excavation tools. A new artificial intelligence (AI) based model has been developed. It utilizes acoustic emission (AE) and rock properties as main indicators of rock abrasivity, estimated by Cerchar Abrasivity Index (CAI). AE sensors are attached to both the Cerchar testing apparatus and the rock in question while conducting scratch tests using hardened steel pins of 42 and 56 HRC. Prior to the implementation of Artificial Neural Network (ANN) modeling, the selection of independent variables was carried out via Gamma test and V-ratio analyses. As a result, AE parameters, such as total number of events and root mean square of signal, in addition to testing parameters (i.e. uniaxial compressive strength, Young׳s Modulus, quartz content and pin hardness) are found to be the optimum model input combination needed to accurately predict CAI.

Statistical evaluation of CERCHAR Abrasivity Index (CAI) measurement methods and dependence on petrographic and mechanical properties of selected rocks of Pakistan

Forty-six rock units selected from various locations of Pakistan comprising of igneous, sedimentary and metamorphic rocks were subject to a comprehensive laboratory testing program consisting of two parts. The first part included CERCHAR abrasivity tests and measurement of physical rock properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), dry density, porosity, and P-wave velocity (V p). The second part involved petroghraphic thin section analyses of 40 selected abrasive and non-abrasive rock samples along with the determination of wear factors such as Schimazek’s F value and Rock Abrasivity Index (RAI). The CERCHAR abrasivity tests were conducted on sawn (CAIs) and freshly broken (CAIfb) rock surfaces employing top and side viewing methods for the measurement of wear flat of the stylus tip. This paper statistically confirms the previous work on the influence of wear flat measurement methods and rock surface conditions on the CERCHAR Abrasivity Index (CAI) values. It was found statistically that CAIs and CAIfb values measured from top view of the test stylus were 17 and 19 % higher, respectively, in comparison to CAIs and CAIfb values measured using side viewing stylus measurement technique. Further the correlations of CAI with RAI, Schimazek’s F value, petrographic, and physico-mechanical rock properties are also discussed.

Study on the Correlations Between Abrasiveness and Mechanical Properties of Rocks Combining with the Microstructure Characteristic

Rock abrasivity has become one of the necessary parameters for mechanical excavation of rock in the tunnelling industry (Wijk 1992; Hassanpour et al. 2011). The Cerchar abrasiveness test is widely used to assess the rock abrasivity. The test features a steel pin of specified shape and quality that is scratched over 10 mm of the specimen’s surface. The pin has a sharp conical point with cone angle of 90 . A static force of 70 N is loaded on the pin and the test is carried out by moving the steel pin at a prescribed velocity on the testing surface. The Cerchar abrasiveness index (CAI) is calculated by multiplying the mean value of the wear flats stated in units of 0.01 mm by 10. The testing velocity is reduced from 10 mm/s of ‘‘Cerchar apparatus’’ to 1 mm/s of ‘‘West apparatus’’ to accommodate the controllability of the testing velocity (West 1981, 1989; Michael et al. 2014). It was concluded in the literature that CAI was not only related to the abrasive mineral content alone but also influenced significantly by the rock strength (AL-Ameen and Waller 1994). The equivalent quartz content (EQC) alonewas found to be not enough to interpret the abrasion values of the Cerchar scratch test (Plinninger et al. 2003). It was found that some technical factors had impact on the test result of the Cerchar scratch test. CAI wasmainly influenced by the rock’s deformability and the content of abrasive minerals (Plinninger et al. 2003). Size of quartz grainswas proved to be very important to the scratch tip wear. Smaller quartz grains gave low wear rate and larger grains gave higher rate (Beste et al. 2004). The relations between CAI value and petrographic properties of Coal Measures Rocks were examined and a good linear relationship between CAI and average quartz grain size parameters was revealed (Yarah et al. 2008). Many factors, including pin hardness, surface condition of specimens, and petrographical and geomechanical properties, were examined to study the influence of various parameters on Cerchar testing (Jamal et al. 2014). So far, there is little knowledge about the quantification of microstructure as well as the effect of microstructure characteristics on the abrasiveness of rocks. Microstructure coefficients are defined and integrated with the mechanical parameters in this study to account for the combined effects on the abrasiveness of rocks. The abrasiveness apparatus used in this study has been improved based on the previous test apparatus. The horizontal movement of the steel pin, which is driven by step-servo motor, can be controlled accurately through software (Fig. 1).

Prediction of abrasiveness index of some Indian rocks using soft computing methods

The present paper mainly describes the prediction methodology to determine the Cerchar Abrasiveness Index and Penetration Rate related to rock excavation using simple geomechanical parameters as predictors. As abrasiveness of rocks is influenced by many geomechanical parameters, an attempt is made to use these parameters for its prediction using Multivariate Regression Analysis and Artificial Neural Networking. Abrasiveness Index as well as Penetration Rate are very vital in deciding the economics of the excavations as they directly govern the wear and tear of drill bit. It was observed that ANN shows a better prediction capability than MVRA using UCS, Point load index, P wave velocity and Young’s modulus as predictors.

Predicting the performance of large diameter circular saws based on Schmidt hammer and other properties for some Turkish carbonate rocks

Schmidt hammer hardness values (SHH1, SHH2, and deformation coefficient) are used to determine the performance of large diameter circular saws. The measured Schmidt hammer hardness values are correlated with the physical and mechanical properties of natural stones and areal slab production rates of large diameter circular saws. Two statistical models for prediction of the areal slab production rates for these machines are developed, which is very important in decision making for engineers. A simple statistical model is suggested taking into account the deformation coefficient and a multiple regression model is also suggested by using the deformation coefficient and Cerchar abrasivity index. The reliability of these models is tested against actual performance of large diameter saws. Verifications and comparisons showed that the models suggested in this study may be a very useful and reliable tool for prediction of areal slab production rates for large diameter circular saws.

Cuttability assessment using the Drilling Rate Index (DRI)

For given geological conditions, cuttability or performance prediction can be expressed by specific cutting force (SFC), specific normal force (SFN), specific energy (SE), and pick wear rate. These parameters are difficult to determine. For this reason researchers try to assess these parameters indirectly by rock mechanical parameters. The Drilling Rate Index (DRI) is the most important input parameter of a commonly used performance prediction model for tunnel boring machines. However, little research has been seen in the literature about assessing cuttability indirectly by DRI. In this study, different types of rock and ore samples were subjected to a small-scale rock cutting test, the Cerchar abrasivity test, the Sievers J miniature drill test, and the brittleness (S 20) test. With the aid of the Sievers and brittleness test results, the DRI parameter was determined. SFC, SFN, and SE, which are the output parameters of small scale rock cutting tests, and the Cerchar abrasivity index values were correlated with Sievers J (S j ) value and DRI. It was seen that SFC, SFN, SE and CAI parameters slightly decreased with increasing Sievers J value and DRI. With these results, it can be stated that DRI and S j could be an alternative to other mechanical rock parameters for assessing cuttability.

The usability of the Cerchar abrasivity index for the evaluation of the triaxial strength of Misis Fault Breccia

The development of some predictive models for the geomechanical properties of fault breccias will be useful for geo-engineers, because the preparation of smooth specimens from the fault breccias is usually tedious and expensive. In this study, simple and multiple regression analysis were applied to the data pertaining to Misis Fault Breccia to develop predictive models for the differential stress (Δσ) from non-destructive methods including the Cerchar abrasivity index (CAI). As a result of simple regression analysis, strong relations between Δσ and indirect properties were not found. The CAI was included in the best multiple regression model for the prediction of Δσ. The significance of derived models was statistically tested. It was concluded that the CAI is a useful property for the prediction of Δσ of Misis Fault Breccia.

沖縄海域の海底熱水鉱床の力学特性と掘削体積比エネルギー

Seafloor hydrothermal deposits have been found to be widespread in the waters off Okinawa Island. Since Japan currently depends heavily on foreign mineral resources, it is expected that such deposits will be developed in the near future. To assist with the design and manufacture of mining and crushing machinery and ore transportation equipment, in this study the mechanical properties of seafloor hydrothermal deposits were investigated. Tests were carried out on the strength, hardness and abrasiveness of boring cores and block samples of the deposits. The average uniaxial compressive strength was found to be between 20 and 60 MPa, the CERCHAR abrasiveness index was between 1 and 2, and the Mohs hardness was between 4 and 6. In uniaxial compression tests, in which a specimen was crushed up to 20% of its initial height, size of debris was a little smaller than tuff, andesite and sandstone. The specific energy calculated with the boring data was found to be closely related to the mechanical properties of the hydrothermal deposits, and its distribution beneath the seafloor was determined from some boring data. The results obtained in this study provide important guidelines for the development of mining techniques for seafloor hydrothermal deposits.