Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques
Abstract Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample prep...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09063-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849388063316246528 |
|---|---|
| author | Sahas V. Swamy Bijay Mihir Kunar Karra Ram Chandar Mamdooh Alwetaishi Shashikumar Krishnan Sudhakar Reddy |
| author_facet | Sahas V. Swamy Bijay Mihir Kunar Karra Ram Chandar Mamdooh Alwetaishi Shashikumar Krishnan Sudhakar Reddy |
| author_sort | Sahas V. Swamy |
| collection | DOAJ |
| description | Abstract Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering. |
| format | Article |
| id | doaj-art-390dda2a75b842979f2f9a14430bb25e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-390dda2a75b842979f2f9a14430bb25e2025-08-20T03:42:25ZengNature PortfolioScientific Reports2045-23222025-08-0115112110.1038/s41598-025-09063-2Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniquesSahas V. Swamy0Bijay Mihir Kunar1Karra Ram Chandar2Mamdooh Alwetaishi3Shashikumar Krishnan4Sudhakar Reddy5National Institute of Technology Karnataka SurathkalNational Institute of Technology Karnataka SurathkalNational Institute of Technology Karnataka SurathkalDepartment of Civil Engineering, College of Engineering, Taif UniversityFaculty of Artificial Intelligence and Engineering (FAIE), Multimedia UniversityDepartment of Mining Engineering, Aditya UniversityAbstract Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering.https://doi.org/10.1038/s41598-025-09063-2Grinding characteristicsHyperparameter optimizationSupervised machine learningPerformance metricsPrediction modelUniaxial compressive strength |
| spellingShingle | Sahas V. Swamy Bijay Mihir Kunar Karra Ram Chandar Mamdooh Alwetaishi Shashikumar Krishnan Sudhakar Reddy Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques Scientific Reports Grinding characteristics Hyperparameter optimization Supervised machine learning Performance metrics Prediction model Uniaxial compressive strength |
| title | Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| title_full | Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| title_fullStr | Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| title_full_unstemmed | Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| title_short | Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| title_sort | prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques |
| topic | Grinding characteristics Hyperparameter optimization Supervised machine learning Performance metrics Prediction model Uniaxial compressive strength |
| url | https://doi.org/10.1038/s41598-025-09063-2 |
| work_keys_str_mv | AT sahasvswamy predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques AT bijaymihirkunar predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques AT karraramchandar predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques AT mamdoohalwetaishi predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques AT shashikumarkrishnan predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques AT sudhakarreddy predictionofuniaxialcompressivestrengthoflimestonefromballmillgrindingcharacteristicsusingsupervisedmachinelearningtechniques |