Unconfined Compressive Strength Of Rock Research Articles

Geostatistical assessment of unconfined compressive strength of rocks in Northeast Jeddah, Saudi Arabia

The determination of rock strength holds paramount importance in the field of engineering geology. In this study, we conduct a comprehensive geostatistical evaluation of the unconfined compressive strength (UCS) within a 100 km2 area situated in northeast Jeddah, Saudi Arabia. The UCS values were indirectly estimated using an empirical equation based on the Schmidt hammer rebound method, resulting in a range of strengths from 9.2 to 198.4 MPa. The corresponding mean UCS values vary between 60.3 to 81.7 MPa, with standard deviations ranging from 18.6 to 45.3 MPa. The analysis revealed that, among the sites examined, a specific location exhibited the highest median UCS value of 72.2 MPa, while another site recorded the lowest value of 56.2 MPa. Based on the distribution of UCS values, the study area was classified into five distinct strength categories: very low, low, medium, high, and very high. Notably, the majority of variability in UCS values was confined within the middle 50% range, as evident from the interquartile range (IQR) below 30 MPa. Additionally, certain sites displayed a tighter cluster of UCS values, while an IQR of DEFJ≥60 indicated a more widespread distribution of strength values. Furthermore, two locations were identified as representing the minimum and maximum UCS values within a 95% confidence interval. The UCS in location A was estimated at 60.25±9.14 MPa, whereas in location B, it was 81.72±8.50 MPa. These findings offer valuable insights into the rock strength characteristics of the designated area, providing essential data for engineering and geotechnical applications.

A Simple and Practical Tool for Indirect Determination of the Unconfined Compressive Strength of Most Common Construction Materials

Determination of the unconfined compressive strength (UCS) of construction materials in the laboratory is tedious and time-consuming. There have been many attempts to indirectly predict UCS using simpler tools and techniques. One of them is the nail gun. The scope of this investigation is to design a nailer which can be applied all construction materials whose UCS range from 1-100 MPa. In the research, rocks, bricks, and concretes prepared in different cement/sand ratios with different strength ranges were used as materials. The unconfined compressive strength of the materials used in the experiments was first determined by conventional compression tests. The nail penetration depths were determined by conducting experiments on the same materials using a nailer with two different energy levels. An empirical relationship was developed by using nail penetration depths, driving energies, and nail diameters as the independent variables and the UCS determined by the conventional method as the dependent variable. According to the empirical relationship determined by multiple regression analysis, the UCS of building materials can be estimated with significance level of 99% by the nail penetration method. The research also revealed that the UCS of rocks might have a coefficient of variation as high as 30%.

Computational intelligence approaches for estimating the unconfined compressive strength of rocks

Computational intelligence approaches for estimating the unconfined compressive strength of rocks

Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks

Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks

Physical model simulation of rock-support interaction for the tunnel in squeezing ground

The existence of squeezing ground conditions can lead to significant challenges in designing an adequate support system for tunnels. Numerous empirical, observational and analytical methods have been suggested over the years to design support systems in squeezing ground conditions, but all of them have some limitations. In this study, a novel experimental setup having physical model for simulating the tunnel boring machine (TBM) excavation and support installation process in squeezing clay-rich rocks is developed. The observations are made to understand better the interaction between the support and the squeezing ground. The physical model included a large true-triaxial cell, a miniature TBM, laboratory-prepared synthetic test specimen with properties similar to natural mudstone, and an instrumented cylindrical aluminum support system. Experiments were conducted at realistic in situ stress levels to study the time-dependent three-dimensional tunnel support convergence. The tunnel was excavated using the miniature TBM in the cubical rock specimen loaded in the true-triaxial cell, after which the support was installed. The confining stress was then increased in stages to values greater than the rock's unconfined compressive strength. A model for the time-dependent longitudinal displacement profile (LDP) for the supported tunnel was proposed using the tunnel convergence measurements at different times and stress levels. The LDP formulation was then compared with the unsupported model to calculate the squeezing amount carried by the support. The increase in thrust in the support was back-calculated from an analytical solution with the assumption of linear elastic support. Based on the test results and case studies, a recommendation to optimize the support requirement for tunnels in squeezing ground is proposed.

Sand Control and Management Strategies in a Low-Permeability Sandstone Oil Field

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31002, “Lessons Learned From Optimizing Sand Control and Management Strategies in a Low-Permeability Sandstone Oil Field,” by Babalola Daramola, SPE, Propellio, and Chidubem Martins Alinnor, SPE, consultant, prepared for the 2021 Offshore Technology Conference, Houston, 16–19 August. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. The complete paper presents lessons learned from optimizing sand control and management strategies for an offshore Nigerian oil field after multiple sanding events and well failures. The authors discuss selection of the previous sand control solution, failure root causes, and remediation options considered. Introduction Field E is in a water depth of 500 ft. It features unconsolidated sands interbedded with siltstones and mud rocks, and was developed with five horizontal production wells (A1 through A5), two horizontal water injection wells (W1 and W2), and two gas injection wells (G1 and G2) drilled from an offshore platform. The produced oil from Field E is exported by pipeline to a storage terminal and exported by shuttle tanker to market. The produced gas is reinjected into the reservoir. Field E achieved first oil production in December 2013 and had produced 43 million STB of crude oil by December 2016 (a current oil recovery factor of 13%). Field E’s development wells were completed with 13Cr 5.5-in. tubing, permanent bottomhole pressure and temperature gauges, gas-lift valves, and chemical-injection valves for scale and hydrate management. Initial Standalone Screen (SAS) Selection and Installation Although core samples were collected from two appraisal wells in Field E, a sand-strength study was not completed before drilling because of existing field analog data and cost-saving reasons. Sand-production prediction was based on the Niger Delta reservoir rule of thumb, which assumes that reservoir sands at depths shallower than 10,000 ft true vertical depth subsea (TVDSS) are unconsolidated and are expected to fail. The target reservoir was at a depth of 9,450 ft TVDSS and was expected to produce sand. Field E’s rock unconfined compressive strength was not measured, with 2,000–8,000 psi assumed based on field analog data. SASs were selected for sand control because most neighboring oil fields used them as well. The complete paper includes failure root-cause analyses of all four wells; this synopsis will include analyses of Wells A2 and A3.

Quantification of statistical uncertainties of unconfined compressive strength of rock using Bayesian learning method

ABSTRACT Due to the sparse data in geotechnical site investigation, statistical characteristics of geo-material properties generally have more or less uncertainties, such as the unconfined compressive strength (UCS) of rock. Based on a site investigation report in Bukit Timah Granite (BTG) formation in Singapore, this paper presented a set of database about UCS from four sites in BTG formation. Subsequently, Bayesian method was applied to quantitatively evaluate the uncertainties of statistical characteristics including the mean, variance, and scale of fluctuation (SOF) of UCS of BTG rocks making use of the available test data. To overcome the limitation of complex analytical solutions, Markov Chain Monte Carlo (MCMC) algorithm is adopted to perform the sampling procedure to obtain the equivalent samples, namely Bayesian-based MCMC method. The results show that the proposed method can be effectively used to quantify the statistical uncertainties and the statistical uncertainties of these three statistical characteristics of BTG rocks are significant. Besides, it is found that the evaluated uncertainties of statistical characteristics to some extent rely on the selection of basic parameters (overall basic parameters or basic parameters of each site) as well as the autocorrelation function (ACF) classes.

Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques

AbstractThe rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen dataset (1300 data points) of well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3%, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4% and 7.9% for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.

Time-Dependent Behavior of the Tunnels in Squeezing Ground: An Experimental Study

The presence of squeezing ground conditions often poses significant challenges in predicting tunnel response over time and to the design of an adequate support system to stabilize the tunnel. Many analytical, empirical, observational, and numerical models have been proposed over the years for the design of tunnels in squeezing ground conditions, but they all have some limitations. The study presented in this paper focused on improving the understanding of tunnel squeezing via a unique physical model test that simulated tunnel boring machine (TBM) excavation in squeezing clay-rich rocks. The physical model included a large true-triaxial cell, a miniature TBM, a laboratory-prepared synthetic test specimen having properties similar to natural mudstone, and instrumentations to monitor deformations around the tunnel boundary during and after the excavation. Experiments were conducted at realistic in-situ stress levels to study time-dependent tunnel convergence in three-dimensions. A tunnel was excavated using the miniature TBM in a cubical rock specimen loaded in the true-triaxial cell; then the confining stress was increased in stages to values above the rock unconfined compressive strength. Strain gauges embedded in the rock specimen and a digital borehole caliper monitored tunnel wall deformations with time. The degree of tunnel squeezing was characterized using a classification system based on tunnel radial strain. A model for time-dependent tunnel longitudinal displacement profile (LDP) was proposed using measurements of the tunnel convergence at different times and different stress levels.

A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock

To design the tunnel excavations, the most important parameters are the engineering properties of rock, e.g., Young’s modulus (E) and unconfined compressive strength (UCS). Numerous researchers have attempted to propose methods to estimate E and UCS indirectly. This task is complex due to the difficulty of preparing and carrying out such experiments in a laboratory. The main aim of the present study is to propose a new and efficient machine learning model to predict E and UCS. The proposed model combines the extreme gradient boosting machine (XGBoost) with the firefly algorithm (FA), called the XGBoost-FA model. To verify the feasibility of the XGBoost-FA model, a support vector machine (SVM), classical XGBoost, and radial basis function neural network (RBFN) were also employed. Forty-five granite sample sets, collected from the Pahang-Selangor tunnel, Malaysia, were used in the modeling. Several statistical functions, such as coefficient of determination (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE) were calculated to check the acceptability of the methods mentioned above. A review of the results of the proposed models revealed that the XGBoost-FA was more feasible than the others in predicting both E and UCS and could generalize.

Dynamic stability analysis of rock tunnels subjected to impact loading with varying UCS

The present paper has been carried out to understand the effects of impact loading on the rock tunnels, constructed in different region corresponding to varying unconfined compressive strength (UCS), through finite element method. The UCS of rockmass has substantial role in the stability of rock tunnels under impact loading condition due to falling rocks or other objects. In the present study, Dolomite, Shale, Sandstone, Granite, Basalt, and Quartzite rocks have been taken into consideration for understanding of the effect of UCS that vary from 2.85 MPa to 207.03 MPa. The Mohr-Coulomb constitutive model has been considered in the present study for the nonlinear elastoplastic analysis for all the rocks surrounding the tunnel opening. The geometry and boundary conditions of the model remains constant throughout the analysis and missile has 100 kg of weight. The general hard contact has been assigned to incorporate the interaction between different parts of the model. The present study focuses on studying the deformations in the rock tunnel caused by impacting load due to missile for tunnels having different concrete grade, and steel grade. The broader range of rock strength depicts the strong relationship between the UCS of rock and the extent of damage produced under different impact loading conditions. The energy released during an impact loading simulation shows the variation of safety and serviceability of the rock tunnel.

Effect of unconfined compressive strength of rock on dynamic response of shallow unlined tunnel

Tunnels and underground space have been evolved as an integrated part of sustainable transportation infrastructure of both the developed and developing countries. Tunnels also have strategic importance in the defence sector of the nation. In recent decades, tunnels have been subjected to several events of intentional and unintentional blast incidents. The present study investigates the response of shallow unlined rock tunnels subjected to an internal blast. The effect of different unconfined compressive strengths (UCS) of rock on the extent of damage produced due to blast loading has been investigated. UCS representing dolomite, shale, sandstone, granite, basalt and quartzite medium has been investigated through a three-dimensional finite element model having dimensions of 30 m × 30 m in cross section and 35 m in length generated in Abaqus. The diameter of the unlined tunnel has been taken as 5 m having overburden depth of 12.5 m. Coupled-Eulerian–Lagrangian modelling has been adopted to simulate the blast loading. Nonlinear Mohr–Coulomb failure criteria have been adopted for different rock media. Trinitrotoluene (TNT) explosive has been used to simulate the blast loading, and Jones–Wilkins–Lee material model has been adopted to model the characteristics of TNT. Subsequently, a relation has been proposed to understand the damage caused by TNT blast loading in a shallow unlined tunnel. The total induced energy due to internal blast loading has been found to be maximum and minimum in dolomite and quartzite, respectively.

Workflow for Sensitivity Analysis and Decision Making on the Lower Limit of the Mud-Weight Window in an Overpressured Formation

Summary When drilling a well, it is important to choose a reasonable mud density to ensure safe operations. This is not a trivial task because a balanced mud-weight has a complex relation to the uncertain formation conditions and properties. This paper presents a method for conducting sensitivity analysis (SA) over the uncertainties involved in the mud-weight window (MWW) assessment, to learn the most important input parameters for the mud density. The uncertainties in the parameters are taken into account using predrill realizations of the formation conditions (vertical stress, minimum horizontal stress, pore pressure, and temperature) and properties (unconfined compressive strength, tensile strength, friction angle, and porosity). The limit pressures of the MWW are computed to avoid shear and tensile failure. The software used to study well stability returns a mud window that could enable underbalanced drilling while still avoiding borehole collapse; this is emphasized by equating the lower stability limit with shear failure and not pore pressure. However, drilling below the pore-pressure limit entails risks with regard to kicks or blowouts. The Mohr-Coulomb failure criterion is used, but other possible shear failure criteria are discussed as well. The output from the SA is used to evaluate how the possibility of gathering more information about the most relevant parameters can improve our knowledge about the appropriate mud density. This is done in the form of value of information (VOI) analysis, coupling the uncertain variables with drilling decisions in a tradeoff between costs and risks. The interest is on deciding what information to acquire when, at a given depth, the drilling operator must decide on whether to keep drilling or set casing. Additional well data are valuable when they can materialize in improved decision making. The case study presented is from an overpressured shale layer in a field in the North Sea. The focus is on the lower allowed well pressure of the mud-weight window for a vertical well, exploring the possibility of underbalanced drilling. The SA shows that the most important uncertainties in this case are pore pressure, unconfined compressive strength, and minimum horizontal stress. The most valuable information-gathering strategy is to observe these main three parameters, which is worth USD 386,980 when acquisition costs are subtracted. The VOI analysis further indicates that data about the rock's unconfined compressive strength are the most valuable in this case, and if the decision maker can afford to acquire only one data type, this should be chosen. Although these results are case specific, we believe that a similar workflow could be useful in other drilling situations.

Mechanical and Physical Based Artificial Neural Network Models for the Prediction of the Unconfined Compressive Strength of Rock

Basalt rocks as building stones were used in many historical buildings in Jordan, and maintenance of these buildings is usually required with time. As part of the effort to collect the necessary information that is needed for future works in repair/strengthen the basaltic structures against any possible future damage. The dry density, Ultrasonic Pulse Velocity, Schmidt Hammer Rebound test, Brazilian Tensile Strength Test, Slake durability, and Point Load test were recorded for specimens tested in the lab to develop indirect methods of estimating the rocks Unconfined Compressive Strength (UCS). Simple regression (SR) analyses were performed to establish correlations between UCS and the results of each above-mentioned rock indices. The SR results showed that a regression model with multiple inputs is needed. In this study, the Back Propagation-Artificial Neural Network (BP-ANN) approach was utilized to predict the USC of Basalt Rock. Two ANN models were developed; one using the physical properties of rocks and the other one using the mechanical properties of rocks. Part of the data collected was used to train the ANN, and a set of independent data was used to validate the developed model. The performance of the ANN model in predicting UCS was compared to that of Multivariate Regression (MVR). The obtained results showed that the ANN model gave higher prediction performance compared to other models. A sensitivity analysis for the developed ANN model was performed to verify the importance of each input. The prediction of UCS can be used to design the proper conservation and repair/strengthen strategies that will allow dealing with the current conditions and the future natural hazards to which these structures are exposed.

GEOTECHNICAL STUDY FOR NEW EGYPTIAN CAPITAL ROCKS

A laboratory study was conducted to develop a database and models for predicting of unconfined compressive strength of rocks in the new administrative capital of Egypt. In this respect, the present study presents correlation equations between the unconfined compressive strength and some mechanical and physical properties of rocks. More than 249 of specimens are prepared and tested; the tests were conducted on four rock types, including basalt, limestone, sandstone, and siltstone. Based on results obtained from the following mechanical and physical tests that were performed on rock samples, unconfined compressive strength, Schmidt hammer, and Brazilian splitting test, they were conducted to determine the mechanical properties of rock specimens, water absorption, and porosity. They were conducted to determine the physical properties of rock specimens. The results of this study indicated that the correlation between unconfined compression strength and the Schmidt hammer test increases as the rock strength. The relationship is found in a polynomial equation. It has been found that the strong linear correlation between unconfined compression strength and Brazilian splitting test for the tested rocks. From this study, it has been found that there is an inverse relationship between the unconfined compression strength and water absorption. This relation is a power equation. It has been found from the present study that the unconfined compression strength increases as the porosity in the rock decreases. The relation is found to be a polynomial equation. The correlation coefficient (R2) varies between 0.5 and 0.95 in all relations.

A Mathematical Model Based on Artificial Neural Networks to Predict the Stability of Rock Slopes Using the Generalized Hoek–Brown Failure Criterion

Stability of rock slopes is a critical issue in many mining and civil engineering projects. The current state of practice for slope stability analysis is based on obtaining the factor of safety (FOS). Stability charts are widely used by engineers to obtain a FOS for a quick assessment of the initial stability of slopes. The stability of rock slopes with vertical walls in urban areas adjacent to existing structures is another important issue in this regard. However, the stability of earth or rock slopes are usually analyzed ignoring surcharge loads. The effect of adjacent structures (as surcharge load) on slope stability (considering these loads) can be very useful in slope stability analyses in urban or even non-urban areas. In the present study, it is tried to investigate the effect of surcharge load on the stability of rock slopes based on the generalized Hoek–Brown failure criterion using a finite element numerical software and the related charts are presented. Since there is a stability chart for each slope angle, a comprehensive mathematical model utilizing artificial neural networks is proposed to predict the stability factor of rock slopes. The independent variables in this study were slope angles, slope height, the intensity of surcharge load, Geological Strength Index (GSI), and unconfined compressive strength of the intact rock. Sensitivity analysis showed that the changes in GSI, effect of surcharge and unconfined compressive strength of rock have the highest effect on the slope stability assurance coefficient.

A method to determine relevant geomechanical parameters for evaluating the hydraulic erodibility of rock

Among the methods used for evaluating the potential hydraulic erodibility of rock, the most common methods are those based on the correlation between the force of flowing water and the capacity of a rock to resist erosion, such as Annandale's and Pells' methods. The capacity of a rock to resist erosion is evaluated based on erodibility indices that are determined from specific geomechanical parameters of a rock mass. These indices include unconfined compressive strength (UCS) of rock, rock block size, joint shear strength, a block's shape and orientation relative to the direction of flow, joint openings, and the nature of the surface to be potentially eroded. However, it is difficult to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock. The assessment of eroded unlined spillways of dams has shown that the capacity of a rock to resist erosion is not accurately evaluated. Using more than 100 case studies, we develop a method to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock in unlined spillways. The UCS of rock is found not to be a relevant parameter for evaluating the hydraulic erodibility of rock. On the other hand, we find that the use of three-dimensional (3D) block volume measurements, instead of the block size factor used in Annandale's method, improves the rock block size estimation. Furthermore, the parameter representing the effect of a rock block's shape and orientation relative to the direction of flow, as considered in Pells' method, is more accurate than the parameter adopted by Annandale's method.

Rock-Strength Analysis and Integrated FEA Modeling Optimize Bit Selection

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 186264, “Rock-Strength Analysis and Integrated FEA Modeling Optimize Bit Selection for Deepwater Exploration Drilling,” by M.R. Yoan Mardiana and Bonar Noviasta, SPE, Schlumberger, prepared for the 2017 SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, Bali, Indonesia, 17–19 October. The paper has not been peer reviewed. Well X is a deepwater exploration well with a carbonate primary target. Traditionally, bit records from offset wells or fields have been the only main source of data for bit selection. Often, bit selection has been trial-and-error because of limited data availability. Now, however, rock-strength-analysis (RSA) software is a requirement for prejob planning to select a bit. Using log data from offset wells, a bit-selection software based on unconfined compressive strength (UCMPS) calculations has been used to optimize the bit selection for Well X. Comprehensive RSA for Bit Selection RSA software generated a comprehensive formation-evaluation and bit-selection analysis for a drilling application. Mud logs from offset wells were used to interpret the rock type. Some electronic logs that have petrographic analysis capability, such as gamma-ray, compressional sonic, density, neutron, and resistivity analysis, quantified the rock appearance and percentage depth-by-depth. The RSA software provided 18 rock types to be analyzed, from sedimentary rocks, such as sandstone, claystone, and limestone, to hard igneous and volcanic rocks. Bits were selected on the basis of their capability to drill a formation with a certain hardness. On the basis of that study, compressional sonic logs converted to shear sonic, combined with the formation type, can be used to calculate the UCMPS of the formation. Apart from drilling parameters, UCMPS is the main contributor to rate of penetration (ROP). UCMPS is also used to calculate abrasion and impact index of the formation. On the basis of the lithology to be drilled, rock UCMPS, formation abrasion, and impact index, the RSA application uses offset-well data from more than 12,500 wells to choose a fixed or roller-cone drill bit with an appropriate combination of cutting structure, gauge protection, hydraulic configuration, and other critical characteristics. With the RSA software, formation characteristics from offset wells can be analyzed. To expand the scope of the formation analysis, several wells from surrounding fields in the same region-al geology also have been correlated and analyzed. A polycrystalline-diamond-compact (PDC) -bit selector in the RSA software considers the blade count of the bit, the cutter size, and the blade profile on the basis of the compressive strength, formation abrasion, and formation impact. The harder the formation is, the software will recommend fewer blades and smaller cutter sizes for the bit to improve durability. If the formation consists mostly of shale and claystone, bit-balling flags will be generated and the bit should have better hydraulic optimization to improve bit cleaning. To reduce the risk of having an under-gauge bit because of abrasion, proper gauge protection should be added to the bit.

Effects of lithofacies and environment on in situ and laboratory Schmidt hammer tests: a case study of carbonate rocks

The Schmidt hammer (SH) test, which is quick, inexpensive, non-destructive and simple to use, has been widely applied to determine hardness and to assess the unconfined compressive strength of rocks. Lithofacies (composition and texture) and environmental conditions (mainly climate) influence the mechanical properties of rock masses as shown by the investigation presented here. To evaluate these effects, a range of carbonate rocks from Al-Ain arid region in the United Arab Emirates were used for extensive in situ and laboratory SH tests. The results expose a wide range of Schmidt hardness rebound (SHR cor ) values for both the in situ and the laboratory tests, but show comparable correlation coefficients between the in situ and laboratory natural surface data. This indicates that field SH measurements can be a simple and reliable tool to obtain data on rock mechanical properties; however, caution should be taken in generalization even in a small locality or a region. Although our data are within the wide range of SH values in the published literature, it is apparent that they represent unique values for the rock type investigated and the environment and thereby provide better constraints for engineers in planning for the safety aspects of constructions.

Estimation of Confined Peak Strength of Crack-Damaged Rocks

It is known that the unconfined compressive strength of rock decreases with increasing density of geological features such as micro-cracks, fractures, and veins both at the laboratory specimen and rock block scales. This article deals with the confined peak strength of laboratory-scale rock specimens containing grain-scale strength dominating features such as micro-cracks. A grain-based distinct element model, whereby the rock is simulated with grains that are allowed to deform and break, is used to investigate the influence of the density of cracks on the rock strength under unconfined and confined conditions. A grain-based specimen calibrated to the unconfined and confined strengths of intact and heat-treated Wombeyan marble is used to simulate rock specimens with varying crack densities. It is demonstrated how such cracks affect the peak strength, stress–strain curve and failure mode with increasing confinement. The results of numerical simulations in terms of unconfined and confined peak strengths are used to develop semi-empirical relations that relate the difference in strength between the intact and crack-damaged rocks to the confining pressure. It is shown how these relations can be used to estimate the confined peak strength of a rock with micro-cracks when the unconfined and confined strengths of the intact rock and the unconfined strength of the crack-damaged rock are known. This approach for estimating the confined strength of crack-damaged rock specimens, called strength degradation approach, is then verified by application to published laboratory triaxial test data.