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<p>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.&#xa0;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.</p>

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Research on the blasting effect prediction and blasting parameter optimization based on PCA-PSO-DBN

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月9日

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  • Weilong Ma,
  • Biao Qiao,
  • Tongkai Wen,
  • Zhanping Song,
  • Zhenzhong Ren

摘要

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.