Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
Currently, the central task of predicting rock fragmentation is becoming increasingly important in the field of rock mechanics and engineering blasting. This direction has been shown to be crucial to ensure the safety and durability of construction projects. In this study, a BP neural network is con...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/6312 |
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| Summary: | Currently, the central task of predicting rock fragmentation is becoming increasingly important in the field of rock mechanics and engineering blasting. This direction has been shown to be crucial to ensure the safety and durability of construction projects. In this study, a BP neural network is constructed to optimize the network weights and bias with the help of CPO algorithm, and its practicality and reliability are tested through the case of an iron ore mine in Hunan Province, China. The model is trained and tested using typical blasting data, and the results show that it performs efficiently, with a short prediction time and a high level of confidence. The predicted values were consistent with actual engineering measurements, achieving an RMSE of only 0.015813, which indicates strong potential for guiding practical blasting block size predictions. |
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| ISSN: | 2076-3417 |