Measurement and prediction of granite damage evolution in deep mine seams using acoustic emission

Du, Sunwen and Feng, Guorui and Li, Zhixong and Sarkodie-Gyan, Thompson and Wang, Jianmin and Ma, Zhenjun and Li, Weihua (2019) Measurement and prediction of granite damage evolution in deep mine seams using acoustic emission. Measurement Science and Technology, 30 (11). p. 114002. ISSN 0957-0233

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Abstract

With unceasing increase of mining depth and development intensity, mining disasters such as rock burst have been increasing frequently, which often result in catastrophic accidents. Therefore, it is imperative to accurately forecast underground disasters. Previous research has suggested that the combination of drill-hole pressure relief and acoustic emission (AE) monitoring serves as an effective measure method towards the forecasting and prevention of disastrous accidents. However, the AE evolution mechanism of underground rock damages remains a challenge; more specifically, the relationships among the drilling hole positions, depths and diameters, and the stress–strain and AE characteristics of the rocks are discussed little in the literature. In order to bridge this research gap, the particle flow code (PFC2D) is employed to systemically investigate the hidden patterns among the mechanical properties, AE and damage evolution of the rock mass with different positions, depths and diameters of the drilling holes. Analysis results demonstrate that the drilling position influences the rock stress–strain and AE characteristics in the plastic deformation stage and the residual stage while the hole depth affects the drilling process. More specifically, the initial AE strength, AE impact at the peak moment, AE fluctuations and induction time are significantly influenced by the drilling position and depth. Furthermore, the drilling position and depth change the evolution law in the damage acceleration and stable development stages, while the hole diameter has little effect on the AE signal during the rock drilling process.

Item Type: Article
Subjects: Scholar Eprints > Computer Science
Depositing User: Managing Editor
Date Deposited: 06 Jul 2023 03:24
Last Modified: 05 Jun 2024 10:36
URI: http://repository.stmscientificarchives.com/id/eprint/2234

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