Uniaxial Compressive Strength Of Rock Research Articles

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.

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

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.

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.

Acoustic and mechanical tests of sandstone-shale composites in Songliao Basin and prediction of uniaxial compressive strength

Rock uniaxial compressive strength is a vital rock mechanics parameter, which accurate assessment is critical for high-efficiency unconventional reservoir development. However, its realization for standard cores is quite problematic due to difficulties and high costs related to the collection and preparation of samples. Unconventional fracturing treatment have evolved from single to composite lithology. This study collected shale and sandstone samples at different bedding angles (0°, 45°, 90°)to prepare the sandstone-shale composite samples at different interlayer ratios. It measured the prepared composites’ longitudinal wave and transverse wave velocities with ultrasonic velocity testing. The uniaxial compressive strength of the sandstone-shale composites with various bedding angles were tested via a servo rock triaxial testing machine. The anisotropic characteristics of the prepared sandstone-shale composites with various bedding angles were analyzed, and relation between the uniaxial compressive strength of the anisotropic sandstone-shale composite and P-wave velocity was derived and experimentally validated. The results show that: the wave velocity test results of the sandstone-shale combination show that with the increasing proportion of shale, the wave velocity of the longitudinal wave and the transverse wave both increase; The wave velocity results of different bedding planes in shale are as follows: Vp(0°)> Vp(45°)> Vp(90°). The average P-wave and S-wave velocities at 45° are 0.92, while the average P-wave and S-wave velocities at 90° are 0.85 compared to 0°; the P-wave and S-wave anisotropy of shale are both 0.14, and the shale shows significant anisotropy. In the sandstone-shale composite, the uniaxial compressive strength gradually decreases and the strain increases with the increase of shale proportion. The value of uniaxial compressive strength is UCS(0°) > UCS(90°) > UCS(45°). Based on the P-wave modulus, a fitting relationship formula for the uniaxial compressive strength of sandstone-shale composites with different bedding angles and interlayer ratios was established and verified. The anisotropic strength predictive formula established based on experimental data is simple, precise, and easy to obtain, with strong practicability. The present research results can provide proper technical support for sandstone-shale composite fracturing optimization and reservoir development.

A comparative study on the development of hybrid SSA-RF and PSO-RF models for predicting the uniaxial compressive strength of rocks

In the field of rock engineering, uniaxial compressive strength (UCS) is a crucial mechanical parameter that cannot be ignored. Due to the difficulty in obtaining high-quality rock core samples in some projects, it is essential to develop a reliable UCS prediction method. In this study, the prediction of UCS was accomplished by employing random forest (RF) models, and two optimizers were used for hyperparameter optimization. A total of 126 cases were collected, which included four input indicators: density, p-wave velocity, Schmidt hammer rebound number, and point load index. In addition, five traditional models, including RF, multiple regression analysis (MR), back propagation neural network (BPNN), extreme learning machine (ELM), and support vector regression (SVR), were introduced for comparison. In order to improve the performance of the models, a five-fold cross-validation method was considered. To evaluate the comprehensive performance of the models, the technique for order preference by similarity to an ideal solution (TOPSIS) method was used, providing an effective approach for model selection. The results showed that the proposed hybrid models can predict the UCS of rocks well. Overall, the SSA-RF model was determined to be the best model for the prediction of the UCS, with values of coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), value accounted for (VAF), Nash-Sutcliffe efficiency coefficient (NSC), and Theil's U-value of 0.9587, 7.0681, 11.8988%, 9.4234, 95.9747, − 2.0781, and 0.0129, respectively. The Gini index indicated that the UCS was more sensitive to the Schmidt hammer rebound number than other indicators. Finally, the UCS prediction models were ranked as follows: SSA-RF, PSO-RF, SVR, RF, MR, BPNN, and ELM. These findings provide valuable insights into the development of accurate and reliable UCS prediction models, which are critical in the engineering practice.

Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density (ρd) in g/cm3, Brazilian tensile strength (BTS) in MPa, and wet density (ρwet) in g/cm3. The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination (R2), root mean squared error (RMSE), variance accounts for (VAF), and a20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS.

Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model

The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity (n,%), Schmidt rebound number (Rn), longitudinal wave velocity (Vp, m/s), and point load strength (Is(50), MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a prediction model of uniaxial compressive strength was built based on the SSA-XGBoost model. To verify the effectiveness of the SSA-XGBoost model, empirical formulas, XGBoost, SVM, RF, BPNN, KNN, PLSR, and other models were also established and compared with the SSA-XGBoost model. All models were evaluated using the root mean square error (RMSE), correlation coefficient (R2), mean absolute error (MAE), and variance interpretation (VAF). The results calculated by the SSA-XGBoost model (R2 = 0.84, RMSE = 19.85, MAE = 14.79, and VAF = 81.36), are the best among all prediction models. Therefore, the SSA-XGBoost model is the best model to predict the uniaxial compressive strength of rock, for the dataset tested.

Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors

Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation (BP) neural network has been widely used in rockburst prediction. However, there are few reports about the influence study of different training sample sizes, optimization algorithms and index dimensionless methods on the prediction accuracy of BP neural network models. Therefore, 100 groups of typical rockburst engineering samples were collected locally and abroad, and considering the relevance, scientificity and quantifiability of the prediction indexes, the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (σθ/σc), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (σc/σt) and the elastic energy index (Wet) were chosen as the prediction indexes. When the number of samples was 40, 70 and 100, sixty improved BP models were established based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, Levenberg–Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, normalized processing method). The prediction performances of each improved model were compared with those of standard BP models. The comparative study results indicate that the sample size, optimization algorithm and dimensionless method have different effects on the prediction accuracy of BP models, which are described as follows: (1) The prediction accuracy value A of the BP model increases with the addition of sample size. The average value Aave of twenty improved models under three kinds of sample sizes increases from Aave (40) = 69.7% to Aave (100) = 75.3%, with a maximal value Amax from Amax (40) = 85.0% to Amax (100) = 97.0%. (2) The value A and comprehensive accuracy value C of the BP model based on four optimization algorithms are generally higher than those of the standard BP model. (3) The improved BP model based on the unified extreme value processing method combined with the Levenberg–Marquardt algorithm has the highest value Amax (100) = 97.0% and value C = 194, and the prediction results of five engineering cases are completely consistent with the actual situation at the site, so this is the best BP neural network model selected in this paper.

A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

Rock point load test (PLT index) and its correlation with rock coring rate of bored pile machine

Rock socketed cast in-situ bored pile has getting more common in the local foundation industry due to high load carrying capacity to support the increasingly large superstructure load. The estimation of bored pile production rate based on rock strength is vital for piling contractor in terms of project planning, site work done forecasting and management of resources. Uniaxial compressive strength (UCS) of rocks is a useful parameter needed for analytical and designing purpose for rock structure, as well as classification of rock strength. Owing to the time-consuming, tedious and expensive testing procedure of UCS, point load test (PLT) on irregular lump samples has appeared to be the best alternative test to ascertain rock strength, which can be performed on-site or off-site. This research was conducted to establish the correlation between rock PLT index and rock coring rate of bored pile machine, where the PLT results and rock coring data were collected from manual recording at respective project site. Graphical relationship was derived for PLT index against rock coring rate for several pile size categories. As rock UCS is typically adopted for foundation design purpose, the conversion factor between PLT index and UCS was derived for the typical bedrock formation encountered in Kuala Lumpur, namely Granitic rock and Limestone. This study also depicts the estimation of mobilized rock unit shaft friction with respect to the corresponding UCS value, using a recommended empirical formula. From this research study, it was justified that PLT could serve as a cost-effective and straightforward field test which provides invaluable guidance for piling production rate estimation as well as better engineering judgement in foundation design.