Rock Mechanics
Barkat Ullah; Raja Khurram Mahmood Khan
Abstract
Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning ...
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Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning (ML) models in forecasting the UCS of sandstone obtained from the Murree and Kamlial formations in the Muzaffarabad area, northwestern Himalayas, Pakistan. The ML models—namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regressor (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were developed to predict UCS (MPa) based on porosity (η), point load index (Is(50)), Schmidt hammer rebound value (Rn), and aggregate impact value (AIV) as input variables. A dataset containing 80 points was divided using a 70:30 split ratio for training and testing sets. K-fold cross-validation (with 5 to 10 folds) was employed to enhance the models' generalization ability. The performance of the models was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). Results revealed that the XGBoost model outperformed the other models, achieving a high R² value of 0.99 and low error values for MAE (0.789), MSE (1.168), and RMSE (1.080). The overall accuracy of the models can be ranked as follows: XGBoost > RF > ANN > ANFIS > SVR. This study provides a benchmark for predicting the UCS of sandstones and similar rocks where complex geology complicates the collection of intact samples.
Exploitation
Tapan Dey; Gopinath Samanta
Abstract
Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of ...
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Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of XGBoost, Random Forests (RFs), Multi Layer Perceptron (MLP), and Gradient Boosting Regression (GBR) models to predict iron ore grades in an Indian mine. An ensemble model was also applied to obtain a more stable grade prediction in the deposit. Models were trained using 4,112 sample data, which have spatial coordinates (east, north, and altitude) and iron grades. The dataset was divided into two parts: 80% (3,289 samples) of the data was used for model training, and 20% (823 samples) was used for model testing. The performance of the models was assessed through the coefficient of determination (R²), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the XGBoost model performs better in the estimation process when compared with other methods, such as RFs, GBR, and MLP. The XGBoost model produced R² of 0.77, MSE of 2.87, and MAPE of 1.8%. The findings indicate that the XGBoost model is effective for predicting iron ore grades in this type of deposit. However, considering geological uncertainty, the application of an ensemble model may be beneficial for grade prediction in an iron deposit.
