Scholarlink

<p>Advance prediction and control of vibration velocity during blasting construction is essential to protect the tunnel and surrounding buildings. In this study, we propose an Improved Particle Swarm Optimization (IPSO) Backpropagation (BP) Neural Network Model (IPSO-BP) considering the effect of frequency (f) to provide a highly accurate prediction tool for predicting the peak particle velocity (PPV) generated from pre-fracture construction of gas-producing tools.The validity and consistency of this model are compared with the traditional particle swarm algorithm optimized BP neural network (PSO-BP), standard BP neural network, empirical formula, genetic-adaptive particle swarm hybrid algorithm optimized BP neural network model (GA-APSO-BP), Gray Wolf Algorithm Optimized Support Vector Regression model (GWO-SVR), Moth Flame Algorithm Optimized BP neural network model (MFO-BP), and the Rungekuta algorithm optimized extreme gradient boosting tree model (RUN-XGBoost) for multi-dimensional comparative analysis. The results show that the IPSO-BP model outperforms other models in prediction performance, both in the training set, testability, and overall. This study proves the reliability and accuracy of the IPSO-BP model in predicting the PPVs, which can contribute to improving the safety of the blasting construction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting peak particle velocity in pre-splitting of gas-producing devices using improved particle swarm optimization algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月21日

查看全文
  • Yawen Cao,
  • Rui Ma,
  • Keji Zhao,
  • Xiaofei Du,
  • Wenjin Liu,
  • Qixiang Gao

摘要

Advance prediction and control of vibration velocity during blasting construction is essential to protect the tunnel and surrounding buildings. In this study, we propose an Improved Particle Swarm Optimization (IPSO) Backpropagation (BP) Neural Network Model (IPSO-BP) considering the effect of frequency (f) to provide a highly accurate prediction tool for predicting the peak particle velocity (PPV) generated from pre-fracture construction of gas-producing tools.The validity and consistency of this model are compared with the traditional particle swarm algorithm optimized BP neural network (PSO-BP), standard BP neural network, empirical formula, genetic-adaptive particle swarm hybrid algorithm optimized BP neural network model (GA-APSO-BP), Gray Wolf Algorithm Optimized Support Vector Regression model (GWO-SVR), Moth Flame Algorithm Optimized BP neural network model (MFO-BP), and the Rungekuta algorithm optimized extreme gradient boosting tree model (RUN-XGBoost) for multi-dimensional comparative analysis. The results show that the IPSO-BP model outperforms other models in prediction performance, both in the training set, testability, and overall. This study proves the reliability and accuracy of the IPSO-BP model in predicting the PPVs, which can contribute to improving the safety of the blasting construction.