High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis

Bei  Jia; Xiao  Wang; Zhongyu  Lv; Zanmin  Xiong; Lulu  Qi

doi:10.59400/sv4216

High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis

Bei Jia
China ENFI Engineering Corporation, Beijing 100038, China; Key Laboratory of Ground Control Management Plan in Deep Metal Mines, National Mine Safety Administration, Beijing 100038, China
Xiao Wang
School of Mechanics and Civil Engineering, China University of Mining Technology, Beijing 100083, China
Zhongyu Lv
China ENFI Engineering Corporation, Beijing 100038, China; Key Laboratory of Ground Control Management Plan in Deep Metal Mines, National Mine Safety Administration, Beijing 100038, China
Zanmin Xiong
China ENFI Engineering Corporation, Beijing 100038, China; Key Laboratory of Ground Control Management Plan in Deep Metal Mines, National Mine Safety Administration, Beijing 100038, China
Lulu Qi
China ENFI Engineering Corporation, Beijing 100038, China; Key Laboratory of Ground Control Management Plan in Deep Metal Mines, National Mine Safety Administration, Beijing 100038, China

Article ID: 4216

Keywords: blasting vibration; peak particle velocity (PPV); prediction model optimization; dimensional analysis; Bayesian theory; model robustness; open-pit mine blasting

Abstract

To address the issues of low fitting accuracy, parameter selection relying on empirical judgment, and difficulties in quantifying model robustness in the traditional Sadovsky blasting vibration prediction formula, this study proposes a method for modifying the peak particle velocity prediction model that balances fitting capability and robustness by integrating Bayesian theory with dimensional analysis. A model prior distribution incorporating multiple on-site blasting parameters is constructed using the dimensional π theorem. Within the Bayesian framework, the maximum likelihood estimation, Occam factor, and posterior credibility of the model are calculated to achieve automatic selection of influencing factors and optimization of the model structure. Based on 88 sets of measured data from an open-pit quarry, with 70 sets used as training samples and 18 sets as validation samples, model training and validation are conducted. The results show that the coefficient of determination R² of the Bayesian modified model increases from 0.7749 obtained by the traditional Sadovsky formula to 0.8576. The Occam factor can effectively characterize the robustness of the model. The preferred model "1 2 4" incorporates empirical formulas for correcting the resistance line, spacing between rows, and borehole diameter. This model achieves an optimal balance between prediction accuracy and robustness, and its prediction stability is significantly superior to that of traditional empirical formulas. This method provides a theoretical basis and engineering reference for accurate prediction and safety control of blasting vibrations.

Published

2026-06-23

How to Cite

Jia, B., Wang, X., Lv, Z., Xiong, Z., & Qi, L. (2026). High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis. Sound & Vibration, 60(3). https://doi.org/10.59400/sv4216

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Issue

Vol. 60 No. 3 (2026)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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