Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning
Abstract Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86827-w |
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| author | Mangalpady Aruna Harsha Vardhan Abhishek Kumar Tripathi Satyajeet Parida N. V. Raja Sekhar Reddy Krishna Moorthy Sivalingam Li Yingqiu P. V. Elumalai |
| author_facet | Mangalpady Aruna Harsha Vardhan Abhishek Kumar Tripathi Satyajeet Parida N. V. Raja Sekhar Reddy Krishna Moorthy Sivalingam Li Yingqiu P. V. Elumalai |
| author_sort | Mangalpady Aruna |
| collection | DOAJ |
| description | Abstract Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices. |
| format | Article |
| id | doaj-art-53f8872b2deb473fb5d4ec25c9f59d61 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-53f8872b2deb473fb5d4ec25c9f59d612025-02-02T12:21:08ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-86827-wEnhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learningMangalpady Aruna0Harsha Vardhan1Abhishek Kumar Tripathi2Satyajeet Parida3N. V. Raja Sekhar Reddy4Krishna Moorthy Sivalingam5Li Yingqiu6P. V. Elumalai7Department of Mining Engineering, National Institute of Technology KarnatakaDepartment of Mining Engineering, National Institute of Technology KarnatakaDepartment of Mining Engineering, Aditya UniversityDepartment of Mining Engineering, Aditya UniversityDepartment of Information Technology, MLR Institute of TechnologyDepartment of Biology, College of Natural and Computational Sciences, Wolaita Sodo UniversityFaculty of Education, Shinawatra UniversityDepartment of Mechanical Engineering, Aditya UniversityAbstract Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices.https://doi.org/10.1038/s41598-025-86827-wIoT-based monitoringGround vibrationMachine learningSurface minesSafety enhancementSafety net |
| spellingShingle | Mangalpady Aruna Harsha Vardhan Abhishek Kumar Tripathi Satyajeet Parida N. V. Raja Sekhar Reddy Krishna Moorthy Sivalingam Li Yingqiu P. V. Elumalai Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning Scientific Reports IoT-based monitoring Ground vibration Machine learning Surface mines Safety enhancement Safety net |
| title | Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning |
| title_full | Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning |
| title_fullStr | Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning |
| title_full_unstemmed | Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning |
| title_short | Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning |
| title_sort | enhancing safety in surface mine blasting operations with iot based ground vibration monitoring and prediction system integrated with machine learning |
| topic | IoT-based monitoring Ground vibration Machine learning Surface mines Safety enhancement Safety net |
| url | https://doi.org/10.1038/s41598-025-86827-w |
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