Applications of Machine Learning Models to Predict the Uniaxial Compressive Strength of Sandstone from Muzaffarabad - A Case Study | ||
| Journal of Mining and Environment | ||
| مقاله 2، دوره 17، شماره 3، مرداد و شهریور 2026، صفحه 781-800 اصل مقاله (3.22 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.16419.3206 | ||
| نویسندگان | ||
| Barkat Ullah* 1، 2؛ Raja Khurram Mahmood Khan3 | ||
| 1Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Zijingang, Hangzhou 310058, China | ||
| 2Computing Center for Geotechnical Engineering, Zhejiang University, Hangzhou, Zhejiang 310058, China | ||
| 3Department of Civil, Construction, Architectural, and Environmental Engineering, University of L'Aquila, L'Aquila 67100, Italy | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| K-fold cross-validation؛ Machine learning؛ Sandstone؛ Uniaxial compressive strength (UCS)؛ XGBoost | ||
| مراجع | ||
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