Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques
Abstract Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33796-7 |
| _version_ | 1827961320327086080 |
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| author | Shahab Hosseini Rashed Pourmirzaee Danial Jahed Armaghani Mohanad Muayad Sabri Sabri |
| author_facet | Shahab Hosseini Rashed Pourmirzaee Danial Jahed Armaghani Mohanad Muayad Sabri Sabri |
| author_sort | Shahab Hosseini |
| collection | DOAJ |
| description | Abstract Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead–zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R2), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R2, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively. |
| first_indexed | 2024-04-09T16:24:53Z |
| format | Article |
| id | doaj.art-23dce44bc5c64ef89ec3c7b24eb9beb1 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T16:24:53Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-23dce44bc5c64ef89ec3c7b24eb9beb12023-04-23T11:16:45ZengNature PortfolioScientific Reports2045-23222023-04-0113112010.1038/s41598-023-33796-7Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniquesShahab Hosseini0Rashed Pourmirzaee1Danial Jahed Armaghani2Mohanad Muayad Sabri Sabri3Faculty of Engineering, Tarbiat Modares UniversityDepartment of Mining Engineering, Urmia University of TechnologyFaculty of Civil Engineering, Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Universiti Teknologi MalaysiaPeter the Great St. Petersburg Polytechnic UniversityAbstract Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead–zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R2), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R2, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively.https://doi.org/10.1038/s41598-023-33796-7 |
| spellingShingle | Shahab Hosseini Rashed Pourmirzaee Danial Jahed Armaghani Mohanad Muayad Sabri Sabri Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques Scientific Reports |
| title | Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques |
| title_full | Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques |
| title_fullStr | Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques |
| title_full_unstemmed | Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques |
| title_short | Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques |
| title_sort | prediction of ground vibration due to mine blasting in a surface lead zinc mine using machine learning ensemble techniques |
| url | https://doi.org/10.1038/s41598-023-33796-7 |
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