Uniaxial Compressive Strength Of Rock Research Articles

Measurement While Drilling Method for Estimating the Uniaxial Compressive Strength of Rocks Considering Frictional Dissipation Energy

Measurement While Drilling Method for Estimating the Uniaxial Compressive Strength of Rocks Considering Frictional Dissipation Energy

Accuracy of Point Load Index and Brazilian Tensile Strength in Predicting the Uniaxial Compressive Strength of the Rocks: A Comparative Study

Uniaxial compressive strength (UCS) of rocks is one of the main parameters required in the design of geotechnical projects such as tunnels, dams, or rock slopes. According to the literature, there are a large number of predictive regression equations to evaluate the UCS from the point load index (PLI) and Brazilian tensile strength (BTS). However, the equations developed in previous studies have different accuracies in UCS prediction. A more accurate prediction of the UCS will result in a more appropriate design of the geotechnical project, and thus ensure its success during operation. In the present paper, a comparative study was conducted between the accuracy of PLI and BTS in predicting the UCS of the limestone and sandstone. Moreover, the role of porosity (n) on the accuracy of predicting the UCS from PLI and BTS was investigated. Some statistical indices were used to investigating the accuracy of predictive regression equations of UCS. The results revealed that the UCS of rocks can be predicted with a higher accuracy using BTS compared with PLI. Also, the findings showed that the n had a significant role in increasing the accuracy of PLI- and BTS-based regression equations of the UCS predictive. The predictive equations established in the present study can be used in practical applications for indirect evaluation of limestone and sandstone UCS in the site of a geotechnical project.

Prediction Method of Rock Uniaxial Compressive Strength Based on Feature Optimization and SSA-XGBoost

In order to establish an optimal model for reasonably predicting the uniaxial compressive strength (UCS) of rocks, a method based on feature optimization and SSA-XGBoost was proposed. Firstly, the UCS predictor system of rocks, considering petrographic and physical parameters, was determined based on the systematic discussion of the factors affecting the UCS of rocks. Then, a feature selection method combining the RReliefF algorithm and Pearson correlation coefficient was proposed to further determine the optional input features. The XGBoost algorithm was used to establish the prediction model for rock UCS. In the process of model training, the Sparrow Search Algorithm (SSA) was used to optimize the hyperparameters. Finally, model evaluation was carried out to test the performance of the UCS prediction model. The method was applied and validated in a granitic tunnel. The results show that the proposed UCS prediction model can effectively predict the UCS of granitic rocks. Compared with simply adopting petrographic or physical parameters as the input features of the model, the UCS predictor considering petrographic and physical characteristics can improve the generalization ability of the SSA-XGBoost UCS prediction model effectively. The prediction method proposed in this study is reasonable and can provide some reference for establishing a universal method for accurately and quickly predicting the UCS of rocks.

Uniaxial compressive strength prediction based on measurement while drilling data: A laboratory study

The uniaxial compressive strength is one of the most important basic parameters of rock, which is essential for surrounding rock stability analysis and support scheme design of underground engineering. At present, it is time consuming and costly to take a large amount of core samples for laboratory testing, and the mechanical properties of the cores may be affected by mining disturbance, which can easily lead to inaccurate result. The measurement while drilling (MWD) technology provides a new approach to solve the above challenge. The key to implementing this technology is to establish a correlation model between drilling parameters and rock mechanics parameters. Based on the characteristics of polycrystalline diamond compact (PDC) bits in drilling and rock breakage, this paper analyzes the mechanical state of the bit in breaking rock. A theoretical correlation model between the torque, feed force of the bit and the uniaxial compressive strength of the rock has been developed. To verify the accuracy of the theoretical model, the uniaxial compressive strength of five different types of rocks (red sandstone, green sandstone, limestone, marble and shale) was obtained through laboratory mechanical tests. The torque Mb, feed force Fb and other parameters in the drilling process of these five rocks were tested through the newly developed MWD test system. The correlation between the drilling parameters and the uniaxial compressive strength of rock was established. The results showed that the feed force Fb and torque Mb measured at five different types of rocks indicate a linear increasing trend with an increase in depth of cut h. Meanwhile, a strong linear relationship between the feed force Fb and torque Mb is evident. This paper proposes an MWD-based method to rapidly conduct the in-situ measurement of the uniaxial compressive strength of various rocks.

The Effect of Mineral Composition on the Correlation between Point Load Index with the Uniaxial Compressive Strength of Sulfate Rocks and their Point Loading Deformation

Point load test is a common, inexpensive and fast test for the indirect achievement of compressive or tensile strength of the rocks in the laboratory and the field. In this research, by conducting uniaxial compression test (UCT), axial (APLT), and diametral (DPLT) point load tests on gathered sulfate rock blocks from the Gachsaran Formation outcrops at the four under-construction reservoir dam sites in Iran, investigate the effect of mineral composition on the relationships between point load index, the uniaxial compressive strength (UCS) and their point load deformation. Regarding that, firstly by creating a correlation between axial (APLI) and diametral (DPLI) point load index and UCS, relationships for each specific mineral composition were provided. Secondly, by comparing the reliability of the APLT and DPLT results in predicting UCS, the conversion factors of the APLI and DPLI to the UCS were calculated. Thirdly, the effect of unique or multiple sampling locations in the analysis results was compared, and finally, for the first time, the deformation of loading points in point load tests was investigated. The results of this study confirmed that by variation of mineral composition of sulfate rocks, the relationships between APLI and DPLI, and UCS, conversion factors as well as loading points deformation patterns during point load tests are changed significantly, and the results of APLT and DPLT can be used to predict UCS with the same reliability in dry and saturated conditions.

Study of the relationship between uniaxial compressive strength vs non-destructive testing and specific weight in bank rocks from the Seybaplaya Campeche Mexico

This research study focuses on analyzing the uniaxial compressive strength of rocks and its correlation with non-destructive testing (Schmidt hammer) and specific gravity. It is important to note that Seybaplaya, located in Campeche, Mexico, is known for its fishing, industrial, and commercial activities. Samples from one of the main active quarries in the state of Campeche were used for the investigation. As a result, equations were developed to predict the simple uniaxial compressive strength based on values obtained from the Schmidt hammer and specific gravity. It is relevant to highlight that these relationships are only valid for rocks with lithological characteristics similar to those used in this study. The results of the analysis and the conclusions demonstrate the non-linearity of the relationships between simple uniaxial compressive strength, non-destructive testing, and specific gravity.

Predicting Mechanical Properties of Carbonate Rocks Using Spectroscopy Across 0.4–12 μm

Determining the mechanical characteristics of rocks is crucial in various civil engineering sectors. Traditionally, the mechanical properties of rocks are determined through on-site and laboratory tests carried out during geotechnical surveys. However, these extensive surveys require considerable time and resources. In contrast, hyperspectral remote sensing techniques offer a rapid and simple means to determine the mineral composition and crystallographic structure of rocks. These features, in turn, influence the rocks' mechanical properties. This study focuses on characterizing the mechanical properties of carbonate rocks in a laboratory setting, using hyperspectral sensors. Approximately 150 cylindrical carbonate rock samples, spanning a wide strength range, were collected from diverse Israeli rock outcrops. Employing a point spectrometer (0.4 to 2.5 µm) and a spectral image sensor (8.0 to 12.0 µm), we captured samples' light reflections and spectral emissivity. Mechanical attributes, including density, porosity, water absorption, and uniaxial compressive strength (UCS), were measured. Advanced data mining techniques identified statistical correlations between hyperspectral signatures and mechanical properties, pinpointing key wavelengths for prediction. The developed models exhibited excellent predictability for the specified properties, attributing accuracy to discernible mineralogy and internal crystalline structure through spectroscopy. However, predicting UCS showed slightly weaker results due to influences from internal flaws not entirely reflected in spectroscopic data. Nonetheless, outcomes regarding rock UCS were deemed satisfactory. These findings open avenues for non-destructive tools in assessing the mechanical properties of rocks in quarrying operations.

Study on the influence of rock bridge angle on the size effect of rock uniaxial compression.

As a basic parameter of rock, the rock bridge angle plays an important role in maintaining the stability of rock masses. To study the size effect of rock bridge angle on the uniaxial compressive strength of rocks, this paper adopts the principle of regression analysis and combines numerical simulation to carry out relevant research. The research results indicate that: (1) the uniaxial compressive strength decreases with the increase of the rock bridge angle, showing a power function relationship; (2) The uniaxial compressive strength decreases with the increase of rock size and tends to stabilize when the rock size is greater than 350 mm, showing a significant size effect. (3) The fluctuation coefficient of compressive strength increases with the increase of rock bridge angle and decreases with the increase of rock size; When the rock size is 350 mm, the fluctuation coefficient is less than 5%; (4) The characteristic compressive strength and characteristic size both increase with the increase of the rock bridge angle.

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

The unconfined compressive strength (UCS) of intact rocks is crucial for engineering applications, but traditional laboratory testing is often impractical, especially for historic buildings lacking sufficient core samples. Non-destructive tests like the Schmidt hammer rebound number and compressional wave velocity offer solutions, but correlating these with UCS requires complex mathematical models. This paper introduces a novel approach using an artificial neural network (ANN) to simultaneously correlate UCS with three non-destructive test indexes: Schmidt hammer rebound number, compressional wave velocity, and open-effective porosity. The proposed ANN model outperforms existing methods, providing accurate UCS predictions for various rock types. Contour maps generated from the model offer practical tools for geotechnical and geological engineers, facilitating decision-making in the field and enhancing educational resources. This integrated approach promises to streamline UCS estimation, improving efficiency and accuracy in engineering assessments of intact rock materials.

Application of Relationship model for the Measurement While Drilling Data to Predict Rock Uniaxial Compressive Strength for Tunneling

Application of Relationship model for the Measurement While Drilling Data to Predict Rock Uniaxial Compressive Strength for Tunneling

A Rock Mass Classification Method for Tuff Tunnel Based on the High-Pressure Gas Expansion Method

A precise rock mass grade result is crucial for directing the tunnel excavation engineering design. A novel rock mass classification method for tuff tunnel based on the high-pressure gas expansion method (HPGEM) was proposed, which was primarily built on field test data previously acquired by the research team. The main achievements are as follows: combined with field data and the HPGEM rock-breaking theory, analyzing the rock uniaxial compressive strength, rock mass integrity index, and the relationship between the gas generator unit consumption and fitted the relevant equations. After that, the rock optimal uniaxial compressive strength (about 150 MPa) and the rock integrity factor (about 0.85) were obtained. With reference to the BQ rock mass classification method, a new rock mass classification method that applied to HPGEM was proposed. This study fills the gap of the appropriate rock mass classification method requested on HPGEM.

Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks

Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks

Prediction of Uniaxial Compressive Strength of Rocks from Their Physical Properties Using Soft Computing Techniques

Prediction of Uniaxial Compressive Strength of Rocks from Their Physical Properties Using Soft Computing Techniques

Microstructural Controls on the Uniaxial Compressive Strength of Porous Rocks Through the Granular to Non‐Granular Transition

AbstractUnder uniaxial compression, a porous rock fails by coalescence of stress‐induced microcracks. The micromechanical models developed to analyze uniaxial compressive strength data consider a single mechanism for the initiation and propagation of microcracks and a fixed starting microstructure. Because the microstructure of clastic porous rock transitions from granular to non‐granular as porosity decreases during diagenesis, their strength cannot be captured by a single model. Using synthetic samples with independently controlled porosity and initial grain radius we show that high‐porosity granular samples, where microcracks grow at grain‐to‐grain contacts, are best described by a grain‐based model. Low‐porosity non‐granular samples, where microcracks grow from pores, are best described by a pore‐based model. The switch from one model to the other depends on porosity and grain radius. We propose a regime plot that indicates which micromechanical model may be more suitable to predict strength for a given porosity and grain radius.

Probabilistic prediction of uniaxial compressive strength for rocks from sparse data using Bayesian Gaussian process regression with Synthetic Minority Oversampling Technique (SMOTE)

Uniaxial compressive strength (UCS) of rocks is one of key rock strength parameters. Generally speaking, UCS can be measured directly through uniaxial compression tests, which is often unfeasible, especially when intact rock samples are highly fragile. Alternatively, the UCS of rocks can be estimated indirectly from other easily available rock indices. Note that adequate measurement data is the prerequisite for the accurate estimation of UCS using indirect methods. This may be difficult to achieve due to the limitation of time and budget, especially for small- to medium-sized projects. In this case, it becomes a challenging issue on how to develop a robust and reliable model for UCS estimation using the sparse measurement data. A fully Bayesian Gaussian process regression (fB-GPR) approach with Synthetic Minority Oversampling Technique (SMOTE) is proposed in this paper to address this problem. A real-life example from Malaysia was used for illustration and validation of proposed method. Results showed that when the synthetic sample size in SMOTE reaches 30 (i.e., optimal synthetic sample size), the coefficient of determination (R2) increases by about 18.92%, and the accuracy of feature selection reaches 98%, compared with the scenario with only sparse measurement data used for fB-GPR model development.

Comparison of artificial intelligence and multivariate regression methods in predicting the uniaxial compressive strength of rock during the specific resistivity monitoring

Comparison of artificial intelligence and multivariate regression methods in predicting the uniaxial compressive strength of rock during the specific resistivity monitoring

Weathering Effects on Engineering Geological Properties of Trachydacitic Volcanic Rocks from the Monte Amiata (Southern Tuscany, Italy)

Variability in lithology and weathering degree affects physical and mechanical properties of rocks. In this study, we investigated the relationships between weathering degree and engineering geological properties of trachydacitic volcanic rocks from Monte Amiata (central Italy) by coupling field and laboratory analyses. We collected in situ Schmidt hammer tests in the field. We evaluated weathering quantifying the percentage of secondary minerals through thermal analysis in the laboratory. We also determined dry density (ρd), specific gravity of solids (Gs), porosity (n) and two-dimensional (2D) porosity as resulted from scanning electron microscopy investigations. The results of our study indicate a negative linear correlation between Schmidt hammer rebound values and secondary mineral percentage. This correlation provides a tool to quantitatively estimate the deterioration of rock uniaxial compressive strength (UCS) as weathering increases. Moreover, thermal analysis turned out to be a quantitative and reproducible method to evaluate weathering degree of magmatic rocks.

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site.

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Uniaxial compressive strength (UCS) of rock is an essential parameter in geotechnical engineering. Point load strength (PLS), P-wave velocity, and Schmidt hammer rebound number (SH) are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS. This study collected 1080 datasets consisting of SH, P-wave velocity, PLS, and UCS. All datasets were integrated into three categories (sedimentary, igneous, and metamorphic rocks) according to lithology. Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types. Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types. UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks. Nonetheless, the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks. The developed predictive models can be applied to quickly predict UCS at engineering sites, which benefits the rapid and intelligent classification of rock masses. Moreover, the importance of SH, P-wave velocity, and PLS were analyzed for the estimation of UCS. SH was a reliable indicator for UCS evaluation across various rock types. P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks, but it was not reliable for assessing the UCS of metamorphic rocks.

Theoretical model for the effect of porosity on correlations of point load tests of rocks

Point load test (PLT) is one of the most adopted indirect ways to estimate uniaxial compressive strength of rocks. Yet, the estimation can be interfered by rock porosity that has rarely been considered in model development. In this study, a theoretical model for the interpretation of results of PLTs considering the effect of porosity is proposed. The model was developed using the elastic stress field inside diametrically loaded sphere within the framework of linear elastic fracture mechanics and requires only basic physical parameters including porosity and Poisson's ratio. Apart from the point load index (PLI), an extra parameter, subtended angle, is included in the interpretation, which accounts partly for the wide range of the ratio between PLI and uniaxial compressive strength observed in experiments. The predictions of the proposed model compare reasonably well with data for a variety of rocks, which demonstrates the validity of the model.