文献类型：期刊文献

英文题名：Prediction method of blasting vibration velocity of high slope based on bayes

作者：Deng, Xiangzhao Nie, Junli Lei, Zhen Zhang, Beibei Chen, Junhao Zhang, Xijiang

第一作者：Deng, Xiangzhao

通信作者：Nie, JL[1]

机构：[1]Guizhou Univ, Coll Resources & Environm Engn, Guiyang 550025, Peoples R China;[2]Guizhou Univ, Key Lab Karst Environm & Geohazard, Minist Land Resources, Guiyang 550025, Peoples R China;[3]Guizhou Inst Technol, Inst Min Engn, Guiyang 550025, Peoples R China;[4]Guiyang Univ, Coll Architectural Sci & Engn, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Coll Resources & Environm Engn, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Univ, Key Lab Karst Environm & Geohazard, Minist Land Resources, Guiyang 550025, Peoples R China.

年份：2026

卷号：16

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-105044863313);WOS:【SCI-EXPANDED(收录号:WOS:001824106100009)】；

基金：This work was supported by the National Natural Science Foundation of China (grant number 42264008) and the National Science and Technology Major Project on Deep Earth Exploration and Mineral Resources Exploration (grant number 2024ZD1002202).

语种：英文

外文关键词：High slope blasting; PPV prediction model; Bayesian; Dynamic update

摘要：Accurate prediction of blasting vibration velocity and effective control of vibration-induced hazards remain key challenges in blasting engineering. Using monitoring data from a high-slope blasting project in the Pan Nan 2 & times; 660 MW low-calorific-value coal comprehensive utilization power generation project, this study develops a Bayesian dynamic updating model for peak particle velocity (PPV) prediction based on the classical Sadovsky formula. Unlike conventional static regression approaches with fixed parameters and limited datasets, the proposed method treats the model parameters K and alpha as random variables and updates their posterior distributions through a Bayesian inference framework as new monitoring data become available. This enables continuous adaptation to complex geological conditions, including joints, faults, weathered zones, and topographic variability, as well as variations in charge configuration and wave propagation characteristics. Results show that as blasting data accumulate, the posterior distributions of the parameters gradually converge, significantly improving model accuracy and robustness. During the updating process, the coefficient of determination (R & sup2;) increases from 0.31 to 0.98, while the root mean square error (RMSE) decreases from 3.38 to 1.18. Independent validation using subsequent monitoring data further confirms the model's stability and generalization capability, with an R & sup2; of 0.97 and an RMSE of 0.5. In comparison, the traditional Sadovsky regression model shows considerably lower performance, with an R & sup2; of - 0.04 and an RMSE of 3.24. The results demonstrate that the proposed Bayesian framework effectively integrates new observations, continuously refines model parameters, and significantly enhances prediction accuracy and reliability. This method provides a robust and adaptive tool for blasting vibration prediction and safety control in complex geological environments, with strong potential for real-time engineering applications.