Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations
This study introduces an innovative approach to predict and mitigate blast-induced vibrations by optimizing blast patterns. By combining a statistical model with the frog algorithm, the method achieves enhanced accuracy and efficiency. Addressing a notable gap in blast engineering, this research uni...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Results in Earth Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211714825000512 |
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| author | Abbas Khajouei Sirjani Farhang Sereshki Mohammad Ataei Manoj Khandelwal Hojatollah Mohammadi Anayi Seyed Mohammad Mehdi Mousavi Nasab Mohammad Amiri Hosseini |
| author_facet | Abbas Khajouei Sirjani Farhang Sereshki Mohammad Ataei Manoj Khandelwal Hojatollah Mohammadi Anayi Seyed Mohammad Mehdi Mousavi Nasab Mohammad Amiri Hosseini |
| author_sort | Abbas Khajouei Sirjani |
| collection | DOAJ |
| description | This study introduces an innovative approach to predict and mitigate blast-induced vibrations by optimizing blast patterns. By combining a statistical model with the frog algorithm, the method achieves enhanced accuracy and efficiency. Addressing a notable gap in blast engineering, this research uniquely integrates statistical models and optimization algorithms for vibration control. Data from 58 blasting events at Golgohar Iron Ore Mine No. 1 were utilized, with 40 datasets used for model training and 18 reserved for independent evaluation. In the prediction phase, four statistical and four AI-based models were developed to estimate peak particle velocity (PPV). Classical evaluation metrics, including R, R², RMSE, MAPE, MAD, and MSE, were applied to identify the best model. The multivariable linear regression model demonstrated superior accuracy, achieving R = 0.94, R² = 0.925, and low error metrics. Following this, the optimization phase employed the multivariable linear regression model as the objective function, integrated with the frog algorithm, to minimize PPV. Several models were developed to assess the influence of algorithmic parameters under the specific conditions of the mine. The results provide a reliable and practical methodology for predicting PPV and optimizing blast patterns, effectively reducing ground vibrations. This straightforward approach offers significant utility for pre-blasting planning and contributes to the advancement of sustainable and efficient blasting practices. |
| format | Article |
| id | doaj-art-0a28df8c6e1d478287d2469daf2aa9e2 |
| institution | Kabale University |
| issn | 2211-7148 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Earth Sciences |
| spelling | doaj-art-0a28df8c6e1d478287d2469daf2aa9e22025-08-20T03:27:48ZengElsevierResults in Earth Sciences2211-71482025-12-01310010910.1016/j.rines.2025.100109Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrationsAbbas Khajouei Sirjani0Farhang Sereshki1Mohammad Ataei2Manoj Khandelwal3Hojatollah Mohammadi Anayi4Seyed Mohammad Mehdi Mousavi Nasab5Mohammad Amiri Hosseini6Faculty of Mining, Petroleum, and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran; Corresponding authors.Department of Mining, Petroleum and. Geophysics, Shahrood University of Technology, Shahrood, IranDepartment of Mining, Petroleum and. Geophysics, Shahrood University of Technology, Shahrood, IranInstitute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia; Corresponding authors.Faculty of Mining Engineering, Zarand Higher Education Complex, Zarand, IranFaculty of Mining Engineering, Zarand Higher Education Complex, Shahid Bahonar University of Kerman, IranResearcher, The Department of Mining and Geology of Research and Technology Management of Gol-e-Gohar, Sirjan, IranThis study introduces an innovative approach to predict and mitigate blast-induced vibrations by optimizing blast patterns. By combining a statistical model with the frog algorithm, the method achieves enhanced accuracy and efficiency. Addressing a notable gap in blast engineering, this research uniquely integrates statistical models and optimization algorithms for vibration control. Data from 58 blasting events at Golgohar Iron Ore Mine No. 1 were utilized, with 40 datasets used for model training and 18 reserved for independent evaluation. In the prediction phase, four statistical and four AI-based models were developed to estimate peak particle velocity (PPV). Classical evaluation metrics, including R, R², RMSE, MAPE, MAD, and MSE, were applied to identify the best model. The multivariable linear regression model demonstrated superior accuracy, achieving R = 0.94, R² = 0.925, and low error metrics. Following this, the optimization phase employed the multivariable linear regression model as the objective function, integrated with the frog algorithm, to minimize PPV. Several models were developed to assess the influence of algorithmic parameters under the specific conditions of the mine. The results provide a reliable and practical methodology for predicting PPV and optimizing blast patterns, effectively reducing ground vibrations. This straightforward approach offers significant utility for pre-blasting planning and contributes to the advancement of sustainable and efficient blasting practices.http://www.sciencedirect.com/science/article/pii/S2211714825000512PPVStatistical modelsArtificial neural networkFrog-leaping algorithmGolgohar mine |
| spellingShingle | Abbas Khajouei Sirjani Farhang Sereshki Mohammad Ataei Manoj Khandelwal Hojatollah Mohammadi Anayi Seyed Mohammad Mehdi Mousavi Nasab Mohammad Amiri Hosseini Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations Results in Earth Sciences PPV Statistical models Artificial neural network Frog-leaping algorithm Golgohar mine |
| title | Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| title_full | Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| title_fullStr | Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| title_full_unstemmed | Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| title_short | Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| title_sort | hybrid statistical algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations |
| topic | PPV Statistical models Artificial neural network Frog-leaping algorithm Golgohar mine |
| url | http://www.sciencedirect.com/science/article/pii/S2211714825000512 |
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