Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP
Abstract The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-67139-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850181653829255168 |
|---|---|
| author | Shuang Liu Enxiang Qu Chun LV Xueyuan Zhang |
| author_facet | Shuang Liu Enxiang Qu Chun LV Xueyuan Zhang |
| author_sort | Shuang Liu |
| collection | DOAJ |
| description | Abstract The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value. |
| format | Article |
| id | doaj-art-718d45c3c79e49528d89eea7def9abdb |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-718d45c3c79e49528d89eea7def9abdb2025-08-20T02:17:50ZengNature PortfolioScientific Reports2045-23222024-10-011411910.1038/s41598-024-67139-xResearch on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BPShuang Liu0Enxiang Qu1Chun LV2Xueyuan Zhang3School of Architecture and Civil Engineering, Qiqihar UniversitySchool of Architecture and Civil Engineering, Qiqihar UniversitySchool of Architecture and Civil Engineering, Qiqihar UniversitySchool of Architecture and Civil Engineering, Qiqihar UniversityAbstract The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.https://doi.org/10.1038/s41598-024-67139-xOpen-pit coal mineBlasting fragmentationKernel principal component analysis (KPCA)Beetle antennae search algorithm (BAS)BP neural network |
| spellingShingle | Shuang Liu Enxiang Qu Chun LV Xueyuan Zhang Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP Scientific Reports Open-pit coal mine Blasting fragmentation Kernel principal component analysis (KPCA) Beetle antennae search algorithm (BAS) BP neural network |
| title | Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP |
| title_full | Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP |
| title_fullStr | Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP |
| title_full_unstemmed | Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP |
| title_short | Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP |
| title_sort | research on the prediction of blasting fragmentation in open pit coal mines based on kpca bas bp |
| topic | Open-pit coal mine Blasting fragmentation Kernel principal component analysis (KPCA) Beetle antennae search algorithm (BAS) BP neural network |
| url | https://doi.org/10.1038/s41598-024-67139-x |
| work_keys_str_mv | AT shuangliu researchonthepredictionofblastingfragmentationinopenpitcoalminesbasedonkpcabasbp AT enxiangqu researchonthepredictionofblastingfragmentationinopenpitcoalminesbasedonkpcabasbp AT chunlv researchonthepredictionofblastingfragmentationinopenpitcoalminesbasedonkpcabasbp AT xueyuanzhang researchonthepredictionofblastingfragmentationinopenpitcoalminesbasedonkpcabasbp |