High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis.
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| Title: | High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis. |
|---|---|
| Authors: | Jia, Bei1,2 jiab@enfi.com.cn, Wang, Xiao3, Lv, Zhongyu1,2, Xiong, Zanmin1,2, Qi, Lulu1,2 |
| 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] |
| Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195090151 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jia%2C+Bei%22">Jia, Bei</searchLink><relatesTo>1,2</relatesTo><i> jiab@enfi.com.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiao%22">Wang, Xiao</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Lv%2C+Zhongyu%22">Lv, Zhongyu</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Xiong%2C+Zanmin%22">Xiong, Zanmin</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Qi%2C+Lulu%22">Qi, Lulu</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2026, Vol. 60 Issue 3, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+analysis%22">Dimensional analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Hypothesis%22">Hypothesis</searchLink><br /><searchLink fieldCode="DE" term="%22Vibration+measurements%22">Vibration measurements</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.59400/sv4216 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Dimensional analysis Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Hypothesis Type: general – SubjectFull: Vibration measurements Type: general Titles: – TitleFull: High-precision blasting vibration prediction model integrating Bayesian theory and dimensional analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jia, Bei – PersonEntity: Name: NameFull: Wang, Xiao – PersonEntity: Name: NameFull: Lv, Zhongyu – PersonEntity: Name: NameFull: Xiong, Zanmin – PersonEntity: Name: NameFull: Qi, Lulu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15410161 Numbering: – Type: volume Value: 60 – Type: issue Value: 3 Titles: – TitleFull: Sound & Vibration Type: main |
| ResultId | 1 |
