Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining
The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the seve...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Rock Mechanics Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773230424000659 |
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| author | Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N. Kushal Kumar Tsuyoshi Adachi P.S. Paul Youhei Kawamura |
| author_facet | Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N. Kushal Kumar Tsuyoshi Adachi P.S. Paul Youhei Kawamura |
| author_sort | Yewuhalashet Fissha |
| collection | DOAJ |
| description | The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE = 35.197 in the PPV prediction, and R2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting. |
| format | Article |
| id | doaj-art-67b7c6e351b24f1e91850c14b4b7da3d |
| institution | Kabale University |
| issn | 2773-2304 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Rock Mechanics Bulletin |
| spelling | doaj-art-67b7c6e351b24f1e91850c14b4b7da3d2025-01-09T06:17:07ZengKeAi Communications Co., Ltd.Rock Mechanics Bulletin2773-23042025-01-0141100166Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in miningYewuhalashet Fissha0Prashanth Ragam1Hajime Ikeda2N. Kushal Kumar3Tsuyoshi Adachi4P.S. Paul5Youhei Kawamura6Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, 010-8502, Japan; Department of Mining Engineering, Aksum University, Aksum, 7080, Tigray, Ethiopia; Corresponding author.School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India; Corresponding author.Department of Systems, Control, and Information Engineering, National Institute of Technology, Asahikawa College, 2-2-1-6 Syunkodai Asahikawa city Hokkaido, 071-8142, JapanSchool of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, 010-8502, JapanDepartment of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, IndiaFaculty of Engineering, Hokkaido University, Kita 8, Nishi 5, Kita-Ku, Sapporo, 0608628, JapanThe vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE = 35.197 in the PPV prediction, and R2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.http://www.sciencedirect.com/science/article/pii/S2773230424000659PPVFrequencyMachine learningBlastingRegressionCatboost |
| spellingShingle | Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N. Kushal Kumar Tsuyoshi Adachi P.S. Paul Youhei Kawamura Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining Rock Mechanics Bulletin PPV Frequency Machine learning Blasting Regression Catboost |
| title | Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| title_full | Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| title_fullStr | Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| title_full_unstemmed | Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| title_short | Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| title_sort | data driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining |
| topic | PPV Frequency Machine learning Blasting Regression Catboost |
| url | http://www.sciencedirect.com/science/article/pii/S2773230424000659 |
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