Prediction of Rock Damage Depth Using Blasting Vibration Monitoring.
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| Title: | Prediction of Rock Damage Depth Using Blasting Vibration Monitoring. |
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| Authors: | Hou, Defeng1,2,3 (AUTHOR) hdf876057557@hotmail.com, Liu, Fuming1,3,4,5 (AUTHOR), Yang, Zhiyong1,3,4,5 (AUTHOR), Guo, Haoyu1,3,4,5 (AUTHOR), Zuo, Ting6 (AUTHOR) ztkust@163.com, Xiang, Biao (AUTHOR) |
| Source: | Shock & Vibration. 7/7/2025, Vol. 2025, p1-16. 16p. |
| Subjects: | Rock slopes, Rock testing, Acoustic vibrations, Slopes (Soil mechanics), Bathymetry |
| Abstract: | This study conducted vibration monitoring and acoustic testing on rock slopes subjected to blasting operations within the elevation range of 988.0–898.0 m at the left bank abutment slot of the Wudongde Hydropower Station. Systematic investigations were performed on the vibration attenuation patterns across nine excavation benches. By incorporating elevation parameters, a regression‐based correlation was established between distance‐dependent peak particle velocity (PPV) measurements and the depth of rock mass damage, thereby facilitating predictive assessments of structural deterioration. The analysis revealed that in slopes characterized by homogeneous lithology and an absence of significant structural discontinuities, PPV induced by bench presplit blasting exhibited a strong exponential correlation with the depth of retaining rock mass damage. Validation of the proposed exponential model demonstrated prediction errors of 9.43% for maximum damage depth and 8.70% for average values when compared with field measurements. The close alignment between predicted and observed data confirms the reliability of this model for estimating blast‐induced rock damage, providing an effective alternative to extensive acoustic testing programs in large‐scale slope engineering projects. [ABSTRACT FROM AUTHOR] |
| Copyright of Shock & Vibration is the property of Wiley-Blackwell 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 |
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