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

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Bibliographic Details
Title:	High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis.
Authors:	Jia, Bei^1,2 jiab@enfi.com.cn, Wang, Xiao³, Lv, Zhongyu^1,2, Xiong, Zanmin^1,2, Qi, Lulu^1,2
Source:	Sound & Vibration. 2026, Vol. 60 Issue 3, p1-16. 16p.
Subjects:	Bayesian analysis, Dimensional analysis, Maximum likelihood statistics, Hypothesis, Vibration measurements
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 p 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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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 p 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. [ABSTRACT FROM AUTHOR]
ISSN:	15410161
DOI:	10.59400/sv4216