Search

The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model's predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The safe and sustainable design of rock slopes, open-pit mines, tunnels, foundations, and underground excavations requires appropriate and reliable estimation of rock strength and deformation characteristics. Cohesion () and angle of internal friction () are the two key parameters widely used to characterize the shear strength of materials. Thus, the prediction of these parameters is essential to evaluate the deformation and stability of any rock formation. In this study, four advanced machine learning (ML)-based intelligent prediction models, namely Lasso regression (LR), ridge regression (RR), decision tree (DT), and support vector machine (SVM), were developed to predict in (MPa) and in (°), with P-wave velocity in (m/s), density in (gm/cc), UCS in (MPa), and tensile strength in (MPa) as input parameters. The actual dataset having 199 data points with no missing data was allocated identically for each model with 70% for training and 30% for testing purposes. To enhance the performance of the developed models, an iterative 5-fold cross-validation method was used. The coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and a10-index were used as performance metrics to evaluate the optimal prediction model. The results revealed the SVM to be a more efficient model in predicting (R2 = 0.977) and (R2 = 0.916) than LR (: R2 = 0.928 and : R2 = 0.606), RR (: R2 = 0.961 and : R2 = 0.822), and DT (: R2 = 0.934 and : R2 = 0.607) on the testing data. Furthermore, to check the level of accuracy of the SVM model, a sensitivity analysis was performed on the testing data. The results showed that UCS and tensile strength were the most influential parameters in predicting and. The findings of this study contribute to long-term stability and deformation evaluation of rock masses in surface and subsurface rock excavations. [ABSTRACT FROM AUTHOR]

• The description parameters of various fractures are proposed in this paper. • A calculation model for the number of sinusoidal fractures is proposed. • A post-processing program is developed to quickly output various fracture parameters. Borehole imaging is one of the main methods for fracture observation in the rock formations, but there is still a lack of identification systems for deriving fracture parameters from borehole images. Additionally, previous research were limited to sinusoidal fractures. In this study, an intelligent system is developed for multiple types of qualitative fracture identification in a quick and robust manner using convolution neural networks and post-processing. Initially, borehole imaging was carried out in six coal mines to develop a borehole image database that includes a wide variety of rock types and fractures. Afterward, the acquired borehole images of fractures were categorized into three types, such as sinusoidal fractures, vertical fractures and broken areas. In this connection, dual convolutional neural networks were developed to recognize fracture type pixels. Meanwhile, the calculation models associated with corresponding fracture parameters were established to quantitatively describe the fracture characteristics. Eventually, the post-processing programs were developed to identify the geometric parameters for each fracture type. The results indicate that the developed dual convolutional neural networks has the capability to detect fracture pixels with a recognition accuracy of 77.1% for each sinusoidal and vertical fracture pixels, and recognication accuracy of 72.9% for broken area pixels. The post-processing program was developed based on Hough transform and a thinning algorithm, that enables it to the the average recognition rate of 88.71% with an average false alarm rate of 16.67% for all three types of fractures. [ABSTRACT FROM AUTHOR]