Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models
Blast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neu...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-10-01
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| Series: | Rock Mechanics Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773230425000447 |
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| author | Shahab Hosseini Abiodun Ismail Lawal Francois Mulenga |
| author_facet | Shahab Hosseini Abiodun Ismail Lawal Francois Mulenga |
| author_sort | Shahab Hosseini |
| collection | DOAJ |
| description | Blast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neural network (BRNN), Bayesian regularized causality-weighted neural network (BRCWNN) and Z-number-based Bayesian regularized causality-weighted neural network (Z-BRCWNN) are proposed in this study for the reliable prediction of BIGV in a dolomitic marble quarry using the obtained field data. The outcome of the proposed models is subjected to rigorous statistical analyses. The outcome of analyses revealed that the Z-BRCWNN model outperformed the other models with 70%, 82% and 82% threshold statistic values evaluated at the 5%, 10% and 15% confidence levels for the testing phase and 63%, 91% and 91% threshold values for the validation phase evaluated at the same levels as above. The sensitivity analysis conducted revealed that the distance from the measuring point to the blasting point (DI) has the highest influence on BIGV. |
| format | Article |
| id | doaj-art-006aed5a3f2247c8b101dff8b2e00de1 |
| institution | DOAJ |
| issn | 2773-2304 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Rock Mechanics Bulletin |
| spelling | doaj-art-006aed5a3f2247c8b101dff8b2e00de12025-08-20T02:56:24ZengKeAi Communications Co., Ltd.Rock Mechanics Bulletin2773-23042025-10-014410021710.1016/j.rockmb.2025.100217Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network modelsShahab Hosseini0Abiodun Ismail Lawal1Francois Mulenga2Department of Mining Engineering, Tarbiat Modares University, Tehran, IranDepartment of Mining Engineering, University of South Africa, Florida Campus Private Bag X6, Johannesburg, 1710, South Africa; Corresponding author.Department of Mining Engineering, University of South Africa, Florida Campus Private Bag X6, Johannesburg, 1710, South AfricaBlast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neural network (BRNN), Bayesian regularized causality-weighted neural network (BRCWNN) and Z-number-based Bayesian regularized causality-weighted neural network (Z-BRCWNN) are proposed in this study for the reliable prediction of BIGV in a dolomitic marble quarry using the obtained field data. The outcome of the proposed models is subjected to rigorous statistical analyses. The outcome of analyses revealed that the Z-BRCWNN model outperformed the other models with 70%, 82% and 82% threshold statistic values evaluated at the 5%, 10% and 15% confidence levels for the testing phase and 63%, 91% and 91% threshold values for the validation phase evaluated at the same levels as above. The sensitivity analysis conducted revealed that the distance from the measuring point to the blasting point (DI) has the highest influence on BIGV.http://www.sciencedirect.com/science/article/pii/S2773230425000447BlastingPPVNeural networkArtificial intelligenceThreshold statisticCosine amplitude method |
| spellingShingle | Shahab Hosseini Abiodun Ismail Lawal Francois Mulenga Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models Rock Mechanics Bulletin Blasting PPV Neural network Artificial intelligence Threshold statistic Cosine amplitude method |
| title | Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models |
| title_full | Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models |
| title_fullStr | Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models |
| title_full_unstemmed | Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models |
| title_short | Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models |
| title_sort | prediction of blast induced ground vibration in dolomitic marble quarry using z number information and fuzzy cognitive map based neural network models |
| topic | Blasting PPV Neural network Artificial intelligence Threshold statistic Cosine amplitude method |
| url | http://www.sciencedirect.com/science/article/pii/S2773230425000447 |
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