Intact Rock Properties Research Articles

Prediction of physico-mechanical rock characteristics from electrical resistivity tests

The indirect estimation of intact rock properties is particularly useful for preliminary investigations in engineering projects. In this paper we examine the usability of electrical resistivity, a nondestructive measurement, for the prediction of physical and mechanical rock characteristics. Physico-mechanical tests (uniaxial compression, Brazilian tensile, density, and porosity tests) and electrical resistivity measurements were performed on specimens of 36 rock types. Before the resistivity tests, the specimens were completely saturated with saline solution. Evaluation of the test results showed that there are medium or strong correlations between resistivity and rock properties. There are also strong or stronger correlations between the two parameters for the rock classes. The regression equations developed were statistically tested, and their validity was confirmed. The results were also compared with previous studies. The conclusion is that electrical resistivity measurement can be used for reliably estimating physical and mechanical rock characteristics.

Reliability analysis of deep tunnels in spatially varying brittle rocks using interval and random field modelling

Rock properties are estimated using objective lab/in-situ testing and subjective judgements invoking different types of uncertainties, i.e., aleatory, and epistemic, along them, often indicated by varying information levels. This study presents a unified reliability method to integrate the spatial variability of inputs modelled via alternate uncertainty models (intervals and probability-boxes (p-boxes)) with those modelled as stochastic variables via probability distributions. The methodology employs advanced Karhunen–Loève decomposition to generate interval and random fields of inputs modelled via alternate and stochastic models, respectively. Input properties are allocated to the zones of the numerical model in Fast Lagrangian Analysis of Continua-2D (FLAC-2D) based on their spatial dependency and correlation functions through a developed MATLAB-FLAC coupled code. The methodology is demonstrated for a deep tunnel in Canada to be constructed along a massive rock prone to brittle failures. Intact rock properties are modelled as stochastic variables due to objective estimation, while Geological Strength Index (GSI) and deformation modulus are modelled using alternate models (interval and p-box, respectively) due to subjective and hybrid estimation (double uncertainty propagation algorithm). The results of the methodology are compared with those of traditional deterministic and random field methods. The methodology reduces the subjectivity invoked by including unavailable additional information (e.g., assuming probability distributions of inputs based on literature) and propagates the originally available information of inputs accurately. The final outputs are the p-boxes of response parameters (i.e., displacements and damage zone) instead of their fixed values (deterministic analysis) and probability distributions (traditional reliability analysis), indicating the propagation of both impreciseness and variability of inputs by the method. For this case study, the p-boxes of outputs were bounding their values/distributions estimated via traditional analyses, verifying the accuracy of the methodology. Further, the impreciseness in the outputs, highest in the damage zone extent, was due to imprecision in the estimated GSI.

Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models.

Estimating penetration rates of Jumbo drills is crucial for optimizing underground mining drilling processes, aiming to reduce costs and time. This study investigates various regression and machine learning methods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structural parameters, was utilized to characterize the rock mass. The dataset was split into 80% for training and 20% for testing. Performance metrics including correlation coefficient (R2), variance accounted for (VAF), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. SVR exhibited the best prediction performance for ROP, achieving the highest R2, lowest RMSE, MAE, and MAPE, as well as the largest VAF values of 0.94, 0.15, 0.11, 4.84, and 94.13 during training, and 0.91, 0.19, 0.13, 6.02, and 91.11 during testing, respectively. With this high accuracy, we conclude that the proposed machine learning algorithms are valuable and efficient predictors for estimating jumbo drill penetration rates in underground mining operations.

A comparative study on the dynamic loading response of heat-treated soft and medium hard rocks

The present study assesses the effects of thermal exposure on the strength, stiffness, and fracture propagation in different rocks subjected to impact loading. The dynamic loading response of soft (synthetic die stone) and medium hard (natural marble) rocks are compared in this study. In the experiments, Split Hopkinson Pressure Bar (SHPB) tests are carried out on the heat-treated rock samples. A high-speed camera records the whole failure process of these specimens, and digital image correlation (DIC) is employed to track the evolution of strain during SHPB compression testing. The variation of mechanical properties of intact rocks with heating temperature is characterized by various thermal zones depending on the transition temperature, which identifies a significant shift in the impact loading response due to heating-induced micro-crack formation. Rocks made of synthetic materials and marble have transition temperatures between 100 and 150 °C and 500 – 650 °C, respectively. In the pre-transition regime, the dynamic compressive strength slightly rises or stays almost unchanged, whereas in the transitional regime, compressive strength falls drastically, and it does not show any vital changes with increasing treatment temperatures in the post-transitional regime. For synthetic rocks and marble, the critical heating durations are 10 and 5 h, respectively, and strength changes are relatively minimal after these heating durations. With increasing temperature (from 30 °C to 300 °C), the fracture pattern of synthetic specimens shifts from shear-tensile-dominated to tensile-dominated followed by shear-dominated. For marble specimens, the fracture pattern shifts from shear-tensile-dominated to shear-dominated as the temperature rises from room temperature to 650 °C.

Estimation of compliance tensor and anisotropy index for fractured rock masses using field measured data

This paper proposes a method for calculating the fractured rocks compliance matrix using field data. The method is based on the Oda fracture tensor, which combines the mechanical properties of intact rocks and the geometric and mechanical properties of the fractures. The compliance matrix-based anisotropy index EAI is derived to evaluate the anisotropy of the fractured rock mass. The value of EAI ranges from 0 to 1.0986. The closer the EAI value is to 0, the higher the degree of anisotropy. According to the fracture parameters measured in the field, the fracture's normal stiffness and shear stiffness values are derived. Then the compliance matrix of the fracture is determined. In addition, the influence of the confining pressure on the normal and shear stiffness of the fracture was studied. The results show that (1) as the spacing and stiffness ratio of the fracture group increase, the EAI of the corresponding fracture rock increases; (2) as the confining pressure increases, it is necessary to apply greater normal stress to produce the same strain and the elastic modulus increases; (3) the larger the stiffness ratio is, the larger the anisotropy index EAI is at the same confining pressure.

A new method for wear estimation of TBM disc cutter based on energy analysis

The accurate estimation of disc cutter wear has always been a long-term topic affecting the excavation efficiency and construction cost of tunnel boring machines (TBMs), especially in the project of high-strength and high-abrasiveness stratum. In fact, disc cutter wear is one of the comprehensive results of rock-machine interaction during TBM excavation. Therefore, the change of internal energy of rock-machine system describes the wear information of disc cutter. In this study, a new method for estimation disc cutter wear based on energy conversion mechanism is proposed. Firstly, the basic relationship of energy conversion during TBM excavation is analyzed in detail, and an energy equation considering the influence of geological conditions, TBM technical specifications and rock-machine interactions at three levels is established. Based on this context, according to the working characteristics and abrasion mechanism of disc cutter, the conversion mechanism between the disc cutter wear energy and wear capacity is investigated, and the definite relationship between the energy conversion coefficient and the properties of intact rock is obtained. Furthermore, to illustrate the applicability of this method, a case study of the Shenzhen Metro Project in China is introduced and analyzed in detail. The results show that the proposed method can predict 85 % of the average wear of each disc cutter on the cutterhead. Finally, the prediction method provided in this study is compared with Gehring, NTNU and CSM models to validate the rationality and reliability of this method. Evidently, this method can provide an accurate and reliable estimate of the general wear capacity and life of disc cutters on the cutterhead, and it is also a very practical tool for TBM engineers to make decision and optimize the cutter replacement scheme.

Upscaling the Mechanical Properties of a Fractured Rock Mass Using the Lattice-Spring-Based Synthetic Rock Mass (LS-SRM) Modeling Approach—Comparison of Discontinuum, Continuum and Empirical Approaches

A numerical characterization of a fractured rock mass and its mechanical behavior using a discontinuum approach was carried out utilizing lattice-spring-based synthetic rock mass (LS-SRM) models. First, LS-SRM models on a laboratory scale were created to reproduce standard rock mechanical tests on Triassic sandstone samples from a quarry in Germany. Subsequently, the intact rock properties were upscaled to an element volume representative for geotechnical applications, recalibrated and combined with a Discrete Fracture Network (DFN) model. The resulting fractured rock mass properties are compared to predictions from empirical relationships based on rock mass classification schemes and the DFN-Oda-Geomechanics approach. Modeling results reveal a significant reduction in the strength of the fractured rock mass compared to the intact rock, showing a high agreement with empirically calculated values. Results for the deformation modulus reveal a significant reduction induced by the fracture network and a good agreement compared to the results obtained by other approaches. It is shown that the LS-SRM allows analyzing the complex mechanical behavior during failure of rock masses, including crack initiation, propagation and coalescence. The resulting rock mass properties are key parameters for a wide range of geotechnical applications and can be used for large-scale numerical modeling as well.

Estimation of mode I static fracture toughness of carbonate rock using small rock fragments

Fracture toughness is the key to analysis of failure progress, which can be defined under different stress conditions as fracture toughness modes. The mode I fracture toughness (KIC) of rock is an important parameter in petroleum and reservoir engineering; however, preparation of standard samples for determination of this parameter is costly and time consuming and is not feasible under all conditions. At times, core samples are not available for such destructive tests. In such situations, the use of small rock fragments for measurement or estimation of the mechanical properties of intact rock can be a practical method. The present study evaluated the single compressive strength test (SCS) as an indirect method for estimating KIC using small rock fragments. Spherical carbonate rock samples of 2–10 mm in diameter from the Asmari Formation were produced from rock blocks and were tested. The behavior of each was studied and the fracture mechanisms were determined for calculation of the amount of stress (σSCS) at the failure point. Semicircular bending tests were used to determine the KIC for each sample and regression models were established between σSCS and KIC where R2 ≥ 0.95. Of the 16 samples studied, 10 were used to develop a regression model and six were used for validation. It was found that the regression models were able to estimate KIC with good accuracy. To evaluate the best model more accurately, the error of estimation of each model was calculated and it was found that the linear and exponential correlations had the lowest errors of estimation. Based on the range of estimated values, linear correlation was selected as the best correlation method. A correlation was made between all σSCS and KIC values for the carbonate rock samples using the linear regression model where R2 = 0.91 and a final empirical relation has been proposed.

Geotechnical Investigations and Support Design for an Underground Powerhouse of Pumped-Storage Power Station: A Case Study in Chongqing, China

This study assesses the efficiency of the empirically recommended supported design of the underground powerhouse of the Panlong pumped-storage power station in Chongqing, China by using 3D distinct element code (3DEC). Field and laboratory tests were conducted to investigate the geological properties of intact rock and rock mass. The results showed that the stability of the large powerhouse may be controlled by the soft rock (mudstone) layers. The rock mass was classified in terms of the Q classification system, basic quality (BQ) method, and hydropower classification (HC) method, and then the supported system was put forward. The efficiency of the designed supported was checked based on the numerical simulation results of deformation and plastic zone. The results showed that the installed support reduces the radius of the plastic zones and the maximum deformation significantly.

Empirical estimation of rock mass deformation modulus of rocks: comparison of intact rock properties and rock mass classifications as inputs

Deformation modulus of rock mass (Em) is an important parameter for the analysis and design of mining engineering projects. However, field tests for measuring deformation modulus of rock mass are difficult, time-consuming, and capital intensive. This has led to the development of numerous empirical models for estimating rock mass deformation modulus, which are in different forms and scattered in the literature. The numerous models available in the literature use different types of inputs. Therefore, this study provides a comprehensive compilation of different empirical models for estimating the deformation modulus of rock masses. The compiled models are grouped based on their type of input parameter(s) into three categories such as those using intact rock properties, rock mass classification indices, and combination of intact rock properties and rock mass classification indices. Then, a comparative analysis was performed using absolute average relative error percentage (AAREP) and variance accounted for (VAF) to assess the reliability of using different types of inputs for estimation of deformation modulus of rock masses using data from two sites. The results of the analyses show that rock mass classification indices are the most reliable indices for estimating the deformation modulus of rock masses among the categories considered for analyses. For AAREP analyses in the two illustrative examples considered in this study, models (7 out of 10) using rock mass classification indices in the estimation of Em have the best performances with AAREP values ranging from 24.07 to 55.15%. For VAF analyses in the two examples, models (8 out of 10) using rock mass classification indices in the estimation of Em have the best performances with values ranging from 59.81 to 88.11%. The lowest errors and highest deviation similarities from models using rock mass classification indices indicate that they produce the most reliable estimations of Em. It is important to note that the reliability of deformation modulus estimated from empirical models depends on the quality of input data as the models performed differently across the sites used in this study. This study therefore provides a compilation of available models for estimating deformation modulus, performance evaluation of available models for estimating deformation modulus, and guidelines for selecting appropriate model for estimating deformation modulus of rock mass.

Investigation of the shear failure of rock joints using the four-dimensional lattice spring model

This research adopts the four-dimensional lattice spring model (4D-LSM) to investigate the shear strength and failure mechanism of irregular rock joints and the influence of rock heterogeneity. The 4D-LSM has been used for a wide range of rock engineering applications, however its ability for simulating direct shear tests has not been investigated in detail. The material strength parameters were calibrated using uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) tests on granite specimens while the joint parameters were calibrated from direct shear tests on saw-cut UCS specimens and rock joints containing a single triangular asperity. From UCS tests, a calibration method is proposed to adjust the excessive machine deformation due to the high brittleness of granite, which is further applied to the direct shear test results. It was found that the strength of rock joints is strongly influenced by the intact rock properties. To investigate the influence of joint geometry and rock heterogeneity, three joint asperity angles and five heterogeneous models were simulated using the calibrated 4D-LSM model. Simulation results were able to capture the influence of different joint asperity angles on the shear strength and failure mechanism as reported in previous experimental and numerical studies. With larger asperity angles, higher shear strengths were obtained. Meanwhile, rock heterogeneity represented by particles with lower strength will reduce the shear strength and alter the failure mechanism, especially at a high porosity. This strength reduction was observed to be greater for steep-angle asperities and becomes less significant for flatter asperities. Similar conclusions were also obtained based on the simulations of natural joint profiles. From these findings, it is suggested that the characterisation of rock heterogeneity is crucial to estimate the strength of rock joints. In addition to the roughness parameters , heterogeneity parameters such as porosity are fundamental for rock engineering applications. • The ability of the (4D-LSM) for simulating the direct shear of irregular rock joints is verified against experimental results. • A calibration is developed for 4D-LSM to model rock joints by integrating uniaxial compressive and direct shear tests. • The influence of rock joint surface on its shear strength is investigated numerically by using the calibrated 4D-LSM. • The influence of different rock heterogeneities on the shear strength of the joints is numerically studied.

A new estimation method and an anisotropy index for the deformation modulus of jointed rock masses

The deformation modulus of a rock mass is an important parameter to describe its mechanical behavior. In this study, an analytical method is developed to determine the deformation modulus of jointed rock masses, which considers the mechanical properties of intact rocks and joints based on the superposition principle. Due to incorporating the variations in the orientations and sizes of joint sets, the proposed method is applicable to the rock mass with persistent and parallel joints as well as that with non-persistent and nonparallel joints. In addition, an anisotropy index AIdm for the deformation modulus is defined to quantitatively describe the anisotropy of rock masses. The range of AIdm is from 0 to 1, and the more anisotropic the rock mass is, the larger the value of AIdm will be. To evaluate the proposed method, 20 groups of numerical experiments are conducted with the universal distinct element code (UDEC). For each experimental group, the deformation modulus in 24 directions are obtained by UDEC (numerical value) and the proposed method (predicted value), and then the mean error rates are calculated. Note that the mean error rate is the mean value of the error rates of the deformation modulus in 24 directions, where for each direction, the error rate is equal to the ratio of numerical value minus predicted value to the numerical value. The results show that (i) for different experimental groups, the mean error rates vary between 5.06% and 22.03%; (ii) the error rates for the discrete fracture networks (DFNs) with two sets of joints are at the same level as those with one set of joints; and (iii) therefore, the proposed method for estimating the deformation modulus of jointed rock masses is valid.

REVIEW ON THE MECHANICAL PROPERTIES OF FROZEN ROCKS

The freezing technique has been employed for a long time to strengthen the mechanical properties of intact rock and rock mass; however, it has not received as much attention as it deserves. This paper thoroughly reviews the effect of freezing on the essential mechanical properties, including uniaxial compressive strength, tensile strength, and Young’s modulus. The laboratory tests include the determination of density, ultrasound speed propagation, and strength parameters such as uniaxial compressive strength, tensile strength, and Young’s modulus. According to previously published results, the strength of different rocks such as marl, limestone, sandstone, tuff, granite, and marble increased significantly due to freezing when the samples were tested in frozen conditions. However, there is variation in strength increase based on rock type. It is outlined here that freezing increases rock strength by a factor of 4 in porous rock and by a factor of 1.8 in crystalline rock. Additionally, Young’s modulus increases with a decrease in temperature; however, a further decrease in temperature from -10 to -20 °C has no effect on Young’s modulus. Moreover, mathematical modelling for frozen rock has been reviewed comprehensively. It was found that porosity, the density of rock grains, density of water, residual unfrozen water content, minimum unfrozen water content at freezing point, material parameters, the initial temperature of rock, crystal size, orientation and alignment of minerals, and the loading rate are the most critical parameters that influence frozen rock strength.

A review on the prediction and assessment of powder factor in blast fragmentation


 
 
 Powder factor can be defined as the quantity of explosives (kg) required to break a unit volume or tonne (t) of rock. The prospect of excavating rocks by blasting is characterized by a specific consumption of explosives. In the past decades, researchers have come up with several precise approaches to predict powder factor or specific charge in blast operations other than through trial blast. Research in this area has focused on the relationship between rock mass properties, blasting material and blasting geometry to establish the powder factor. Also, the interaction between specific energy and particle size embodied in the theory of comminution that is less dependent on local conditions has been studied. In this paper, the various methods for powder factor estimation based on empirical and comminution theory modelling as well as machine learning approaches in both surface bench blasting and underground tunnel operations have been reviewed. The influence of intact rock properties on powder factor selection and the influence of powder factor selection on post-blast conditions have also been discussed. Finally, the common challenges that have been encountered in powder factor estimations have been pointed out in this regard.
 
 

The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach

The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEP-LR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies.

Upscaling of fractured rock mass properties – An example comparing Discrete Fracture Network (DFN) modeling and empirical relations based on engineering rock mass classifications

In this study a comparison of Discrete Fracture Network (DFN) modeling and empirical relationships based on conventional engineering rock mass classifications is carried out in order to determine the elastic properties of fractured rock masses. Uncertainties are considered by a stochastic description of the fracture network geometry and a probabilistic approach, utilizing Monte Carlo simulation techniques, for the various other input parameters. The proposed workflow is applied to two sandstone outcrops. Geometric information on the fracture systems is obtained from terrestrial laser scanning providing the input parameters (orientation, size and intensity) for each fracture set including probability density functions required for DFN modeling. After adding the mechanical properties of intact rock and fractures obtained by laboratory tests, the DFN approach allows to calculate values for rock mass deformation modulus and Poisson's ratio assuming either isotropy or vertical-transverse isotropy. These estimates are compared to values derived from empirical relationships based on engineering rock mass classifications. The comparison of the two approaches and, in particular, of the different empirical relationships reveals a significant scatter in the estimated rock mass properties. Only those empirical equations which incorporate intact rock properties in addition to the classification score yield plausible results, i.e., a pronounced weakening due to the fractures, and are in agreement with the estimates of the DFN approach. The additional effort of generating a DFN model shows its benefits if an anisotropic rock mass and/or spatially varying properties have to be considered, thus, providing a comprehensive hydromechanical characterization of anisotropic fractured rock masses.

A temperature-dependent mechanical model to assess the stability of degrading permafrost rock slopes

Abstract. Over the last 2 decades, permafrost degradation has been observed to be a major driver of enhanced rock slope instability and associated hazards in high mountains. While the thermal regime of permafrost degradation in high mountains has been addressed in several modelling approaches, no mechanical models that thoroughly explain rock slope destabilisation controls in degrading permafrost have been developed. Meanwhile, recent laboratory studies have shown that degrading permafrost affects both, rock and ice mechanical strength parameters as well as the strength of rock–ice interfaces. This study presents a first general approach for a temperature-dependent numerical stability model that simulates the mechanical response of a warming and thawing permafrost rock slope. The proposed procedure is exemplified using a rockslide at the permafrost-affected Zugspitze summit crest. Laboratory tests on frozen and unfrozen rock joint and intact rock properties provide material parameters for discontinuum models developed with the Universal Distinct Element Code (UDEC). Geophysical and geotechnical field surveys reveal information on permafrost distribution and the fracture network. This model can demonstrate how warming decreases rock slope stability to a critical level and why thawing initiates failure. A generalised sensitivity analysis of the model with a simplified geometry and warming trajectory below 0 ∘C shows that progressive warming close to the melting point initiates instability above a critical slope angle of 50–62∘, depending on the orientation of the fracture network. The increase in displacements intensifies for warming steps closer to 0 ∘C. The simplified and generalised model can be applied to permafrost rock slopes (i) which warm above −4 ∘C, (ii) with ice-filled joints, (iii) with fractured limestone or probably most of the rock types relevant for permafrost rock slope failure, and (iv) with a wide range of slope angles (30–70∘) and orientations of the fracture network (consisting of three joint sets). Here, we present a benchmark model capable of assessing the future destabilisation of degrading permafrost rock slopes.

Prediction of physico-mechanical properties of intact rocks using artificial neural network

Prediction of physico-mechanical properties of intact rocks using artificial neural network

Introducing a new classification of soft rocks based on the main geological and engineering aspects

Soft rock masses represent a significant percentage of rock material on the earth’s crust. The engineering properties of these rocks are related to their composition, geological history, and degree of weathering. Mudrocks (claystone, mudstone, argillite), marl, shale, sandstone, and conglomerates with weak cementing material are the most common example of these rocks which can be subjected to a wide variation of their engineering properties. Generally, they can be grouped in a broad sense, as geological materials with very low intact rock properties, namely, high deformability and low strength that span the range between soils and hard rocks. This paper is organized to suggest a classification for these rocks, dealing with the main geological and engineering aspects and features that characterize these materials. The two indices of soft rock number (SRN) and soft rock activity (SRA) have been introduced in this classification. The SRN is obtained based on the geomechanical properties of soft rocks, and it is within a range from 0 to 100 (0 for very weak rocks and 100 for strong ones). On the other hand, the SRA index, which indicates how rocks respond to disturbance, is calculated using an empirical graph. This index can have values from 0 to 1. If the index is closer to 0, the rock mass will be less sensitive to disturbance. Based on the two indices, the weakest group of soft rocks, which is called swelling soft rocks, has SRNs less than 20 and SRAs higher than 0.8. Excavation of these rocks can be accompanied by various types of failures. Moreover, in high-strength soft rocks, SRNs and SRAs have values higher than 80 and lower than 0.25, respectively. These rocks have good geomechanical properties and the lowest sensitivity to disturbance, so excavation them results in the fewest failures.

Introducing an empirical model for prediction of disc cutter life for TBM application in jointed rocks: case study, Kerman water conveyance tunnel

Disc cutters wear and need to be replaced when using tunnel boring machines (TBMs) to excavate through rock, often becoming a high ticket item on the list of consumables in tunneling operation. The empirical models used for predicting cutter life are commonly based on the properties of intact rock, while the impact of rock mass characteristics on cutter wear is often ignored or not properly taken into account. Joints and discontinuities tend to decrease the strength of rock mass, as compared to intact rock. Joint properties can affect the interactions between the rock and the disc cutter in the boring process. In other words, the cutter wear and life in a jointed rock mass are different than the wear rate in intact rock. In this study, field data including the geomechanical parameters and observed cutter life in the Kerman water conveyance tunnel (KWCT) project was analyzed using multivariable regression to offer a new empirical prediction model for prediction of cutter life in the jointed rocks. The model presented in this study shows that cutter life is a function of the total fracturing factor and rock mass strength, representing rock mass properties and Cerchar abrasivity index as intact rock property. Furthermore, boring in jointed rock is easier than an intact rock, and as a result, the cutter life increases. The validation results show that this model can offer a more accurate prediction of the disc cutter life in tunneling projects involving jointed rock masses.