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

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 frequ...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 13663 - 17
Main Authors: Cao, Yawen, Ma, Rui, Zhao, Keji, Du, Xiaofei, Liu, Wenjin, Gao, Qixiang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21.04.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/986
639/705/117
639/705/794
Algorithms
Comparative analysis
Frequency
Humanities and Social Sciences
Improved particle swarm algorithm
multidisciplinary
Neural networks
PPV prediction
Predictions
Production appliance pre-splitting
Relationship strength
Science
Science (multidisciplinary)
Velocity
Frequency
PPV prediction
Improved particle swarm algorithm
Production appliance pre-splitting
Relationship strength
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-97806-6