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A Data-Driven Method for Predicting Rocks' Young’s Modulus: Case Study
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DOI：10.1007/s00603-026-05419-w.png)
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Abstract
En 中文
Young's modulus (E) is a crucial parameter for predicting a material's ability to withstand pressure and is essential in designing rock engineering projects. E has wide applications in mining, geotechnical engineering, and other fields. While E can be measured directly through laboratory tests, this requires high-quality core samples and expensive modern equipment. Therefore, an indirect method for estimating E is an attractive alternative. In this study, four novel data-driven approaches—ridge regression (RR), Lasso regression (LR), artificial neural network (ANN), and gradient boosting regressor (GBR)—were developed to predict E. The dataset of E was divided into 70% for training and 30% for testing for each model. To improve the performance of each model, an iterative fivefold cross-validation method was used. Results showed that the GBR regression model outperformed the other models, achieving higher accuracy with a correlation coefficient (R2) of 0.995 on the training set and 0.992 on the testing set, mean absolute errors (MAE) of 0.0162 and 0.0147, respectively, and root mean square errors (RMSE) of 0.02 and 0.0173. The model also scored highly on the a20-index, with 0.96 at training and 0.98 at testing. Using these four regression models, this study provides alternative methods to predict E accurately and efficiently. Highlights
Keywords:
Young’s modulus
Data-driven approach
Gradient boosting regression
Mining rock engineering
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