Young’s modulus (E) is essential for predicting the behavior of materials under stress and plays an important role in the stability of surface and subsurface structures. E has a wide range of applications in mining, geology, civil engineering, etc.; for example, coal and metal mines, tunnels, foundations, slopes, bridges, buildings, drilling, etc. This study developed a novel machine learning regression model, namely an extreme gradient boosting (XGBoost) to predict the influences of four inputs such as uniaxial compressive strength in MPa; density in g/cm³; p-wave velocity (Vp) in m/s; and s-wave velocity in m/s on two outputs, namely static Young’s modulus (E_s) in GPa; and dynamic Young’s modulus (E_d) in GPa. Using a series of basic statistical analysis tools, the accompanying strengths of each input and each output were systematically examined to classify the most prevailing and significant input parameters. Then, two other models i.e., multiple linear regression (MLR) and artificial neural network (ANN) were employed to predict E_s and E_d. Next, multiple linear regression and ANN were compared with XGBoost. The original dataset was allocated as 70% for the training stage and 30% for the testing stage for each model. To improve the performance of the developed models, an iterative 10-fold cross-validation method was used. Therefore, based on the results XGBoost model has revealed the best performance with high accuracy (E_s: correlation coefficient (R²) = 0.998; E_d: R² = 0.999 in the training stage; E_s: R² = 0.997; E_d: R² = 0.999 in the testing stage), root mean square error (RMSE) (E_s: RMSE = 0.0652; E_d: RMSE = 0.0062 in the training stage; E_s: RMSE = 0.071; E_d: RMSE = 0.027 in the testing stage), RMSE-standard deviation ratio (RSR) index value (E_s: RSR = 0.00238; E_d: RSR = 0.00023 in the training stage; E_s: RSR = 0.00304; E_d: RSR = 0.001 in the testing stage) and variance accounts for (VAF) (E_s: VAF = 99.71; E_d: VAF = 99.99 in the training stage; E_s: VAF = 99.83; E_d: VAF = 99.94 in the testing stage) compared to the other developed models in this study. Using a novel machine learning approach, this study was able to deliver substitute elucidations for predicting E_s and E_d parameters with suitable accuracy and runtime.

The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block‐IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρ_w) in g/cm³; moisture in %; dry density (ρ_d) in g/cm³; and Brazilian tensile strength (BTS) in MPa. Then, 106‐point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R²) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R² = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρ_w are positively correlated, and the moisture and ρ_d are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block‐IX at Thar Coalfield, Pakistan.

Drilling rate index (DRI) is a fundamental parameter in the investigation of rock drillability, as drillability is considered one of the main problems in rock engineering. Several researchers have continuously tried to analyze and correlate rock DRI, but the problem remains unchanged. This study elucidates the machine learning approaches, namely long short term memory (LSTM), simple recurrent neural network (RNN) and random forest algorithm (RFA) to predict DRI of rocks using multivariate inputs, that is, uniaxial compressive strength in MPa; Brazilian tensile strength (BTS) in MPa; brittleness value (S₂₀); Sievers’ J value (S_j); modulus ratio (MR); shore hardness (SH), porosity (n) in %; shimazeks F abrasitivity in N/mm; and equivalent quartz content in %. For all proposed methods, the original dataset was divided into 70% for training and the remaining 30% for testing. Next, the performance indices, such as correlation coefficient (R²), root mean square error (RMSE), variance accounts for (VAF) and a-20 index of each proposed method were determined to examine the accuracy of the predicted data. In this study, according to the results of LSTM, simple RNN and RFA methods, the LSTM revealed the best prediction output for DRI with the strongest R², the lowest RMSE, the largest VAF and an appropriate a-20 index values as 0.999, 0.13416, 0.997, and 0.999 in the training stage and 0.998, 0.19479, 0.996, and 0.997 in the testing stage, respectively. Therefore, LSTM is an applicable machine learning approach that can be applied to accurately predict the DRI.

The coal mining, being highly susceptible to accidents and fatalities, is considered amongst the most dangerous occupations. Historically, Pakistan has faced an alarmingly high rate of coal mine disasters. There is a general lack of country-wide data and subsequent research on the fatalities of coal miners in Pakistan. In this study, a multimodel statistical analysis of underground coal mine fatal accidents in Pakistan from 2010 to 2018 was carried out. The results indicate a total of 53 accidents resulting in 312 fatalities, where mine collapse caused 36% accidents and 51% fatalities. It is followed by gas explosion (19% accidents and 17% fatalities) and mine blasting (13% accidents and 16% fatalities). A positive correlation (0.71) exists between accidents and fatalities. Furthermore, mine collapse ( $\text{~}$8.42) and mine blasting ( $\text{~}$6.86) have the highest rate of fatalities per accident. Pakistan, with $\text{~}$108.86%, has an alarmingly high average annual surge in rate of fatalities per million tons of coal production as compared to India (~-0.46%) and China ( $\text{~}$-23.81%). Finally, effective safety recommendations are proposed to reduce the fatalities in the underground coal mines of Pakistan.

In coal mining operations, the stable operation of hydraulic supports is crucial for ensuring mine safety. However, the nonlinear, non-stationary characteristics and noise interference in hydraulic support pressure data pose significant challenges for anomaly detection and fault diagnosis. This study proposes an anomaly detection and failure identification method based on Gated Recurrent Unit Autoencoder (GRU-AE), aimed at achieving anomaly detection in hydraulic support pressure data and equipment failure early warning. Through in-depth analysis of data from two coal mines in China, we systematically evaluated the model’s key parameters. The study revealed that window size had a limited impact on model performance, with a window length of 144 demonstrating optimal comprehensive performance in both anomaly detection and failure mode identification. The study also investigated the effectiveness of teacher forcing techniques. Although this technique can accelerate model convergence, it may lead to training instability and reduced generalization capability, requiring careful consideration in practical applications. Our proposed Recurrent Reconstruction Network model demonstrated excellent performance in complex coal mine hydraulic support data, effectively identifying anomalous regions and potential equipment failure characteristics while revealing potential deviations between model predictions and actual data, demonstrating its superior learning capability for periodic data patterns and equipment failure characteristics. Experimental results validated the effectiveness of the GRU-AE model in hydraulic support pressure anomaly detection and equipment fault diagnosis, providing an innovative technical solution for coal mine safety monitoring.