Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN
Abstract In mountain tunnel blasting construction, challenges such as over-excavation and improper particle size distribution are frequently encountered. Traditional neural network prediction models and empirical formulas have proven inadequate for optimizing construction parameters. To improve the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07094-y |
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| _version_ | 1849691345581506560 |
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| author | Weilong Ma Biao Qiao Tongkai Wen Zhanping Song Zhenzhong Ren |
| author_facet | Weilong Ma Biao Qiao Tongkai Wen Zhanping Song Zhenzhong Ren |
| author_sort | Weilong Ma |
| collection | DOAJ |
| description | Abstract In mountain tunnel blasting construction, challenges such as over-excavation and improper particle size distribution are frequently encountered. Traditional neural network prediction models and empirical formulas have proven inadequate for optimizing construction parameters. To improve the accuracy of prediction models, this study employed Principal Component Analysis (PCA) to identify three key factors influencing construction outcomes as input variables for the model. Additionally, Particle Swarm Optimization (PSO) was integrated for parameter adjustment, leading to the optimization of the parameters within the Deep Belief Network (DBN) model. Two PCA-PSO-DBN models were developed, specifically addressing tunnel over-excavation and the equivalent size of crushed rocks. By training and predicting data from Sect. 3 of the NEOM New City tunnel project, the feasibility of the model was validated through on-site data analysis. The results indicated that compared to traditional DBN and PCA-DBN models, the proposed model reduced maximum errors by 16.98, 7.68, and 11.37, 4.85%, respectively, demonstrating higher precision. Following blasting parameter optimization, the reductions in maximum linear over- or under-excavation and the equivalent size of crushed rocks in the tunnel reached 40.94% and 18.70%, respectively, compared to the original blasting plan. This model introduces innovative methodologies and possibilities, offering valuable insights and references for similar endeavors. |
| format | Article |
| id | doaj-art-db979d9abfe044279f829d146c4867fb |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-db979d9abfe044279f829d146c4867fb2025-08-20T03:21:03ZengSpringerDiscover Applied Sciences3004-92612025-06-017612210.1007/s42452-025-07094-yResearch on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBNWeilong Ma0Biao Qiao1Tongkai Wen2Zhanping Song3Zhenzhong Ren4CSCEC InternationalChina Construction Third Engineering Bureau Group Co., Ltd.China Construction Seventh Engineering Division Corp., Ltd.School of Civil Engineering, Xi’an University of Architecture and TechnologySchool of Future Technology, Xi’an University of Architecture and TechnologyAbstract In mountain tunnel blasting construction, challenges such as over-excavation and improper particle size distribution are frequently encountered. Traditional neural network prediction models and empirical formulas have proven inadequate for optimizing construction parameters. To improve the accuracy of prediction models, this study employed Principal Component Analysis (PCA) to identify three key factors influencing construction outcomes as input variables for the model. Additionally, Particle Swarm Optimization (PSO) was integrated for parameter adjustment, leading to the optimization of the parameters within the Deep Belief Network (DBN) model. Two PCA-PSO-DBN models were developed, specifically addressing tunnel over-excavation and the equivalent size of crushed rocks. By training and predicting data from Sect. 3 of the NEOM New City tunnel project, the feasibility of the model was validated through on-site data analysis. The results indicated that compared to traditional DBN and PCA-DBN models, the proposed model reduced maximum errors by 16.98, 7.68, and 11.37, 4.85%, respectively, demonstrating higher precision. Following blasting parameter optimization, the reductions in maximum linear over- or under-excavation and the equivalent size of crushed rocks in the tunnel reached 40.94% and 18.70%, respectively, compared to the original blasting plan. This model introduces innovative methodologies and possibilities, offering valuable insights and references for similar endeavors.https://doi.org/10.1007/s42452-025-07094-ySmooth tunnel blastingPrincipal component analysisParticle swarm optimizationDeep belief networkBlasting parameters |
| spellingShingle | Weilong Ma Biao Qiao Tongkai Wen Zhanping Song Zhenzhong Ren Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN Discover Applied Sciences Smooth tunnel blasting Principal component analysis Particle swarm optimization Deep belief network Blasting parameters |
| title | Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN |
| title_full | Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN |
| title_fullStr | Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN |
| title_full_unstemmed | Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN |
| title_short | Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN |
| title_sort | research on the blasting effect prediction and blasting parameter optimization based on pca pso dbn |
| topic | Smooth tunnel blasting Principal component analysis Particle swarm optimization Deep belief network Blasting parameters |
| url | https://doi.org/10.1007/s42452-025-07094-y |
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