大尺度灰岩张拉破坏过程声发射信号及应变演化特征研究

Study on the evolution of acoustic signals and strain evolution during large-scale tensile failure test of limestone

摘要

【目的】张拉破裂是岩体崩塌孕灾致灾过程中的主要破裂形式，研究岩体张拉破坏过程中的声发射信号特征和应变演化规律，识别岩体破坏前兆信息，对岩体崩塌监测预警具有重要的意义。【方法】通过开展大尺度灰岩三点弯曲试验(1.0 m×0.5 m×0.15 m),利用声发射技术和数字图像相关技术实时监测破裂过程，结合主成分分析和层次聚类算法分析大尺度灰岩张拉破坏过程中的声发射信号和变形演化特征。【结果】结果显示：声发射监测技术能很好的识别岩体的破坏状态，并提供有效的破坏前兆信息。【结论】结果表明：(1)根据声发射特征参数可将岩石的张拉破坏阶段分为：微裂纹萌生阶段、小尺度裂纹稳定扩展阶段、裂纹不稳定扩展阶段和破坏阶段共四个阶段；(2)采用主成分分析和层次聚类算法将大尺度岩石张拉破坏过程的声发射信号划分为六个类别，其中渐变低幅值型信号和突发高幅值型信号两类特征信号的变化特征可表征岩石破裂所处发展阶段；(3)声发射特征参数及其演化特征可有效反映岩石破裂过程，相比同时进行的变形监测可为岩石的破坏提供提前141 s的前兆信息。研究结果可为岩体崩塌监测预警方法和技术提供支撑。

Abstract

[Objective] Tension rupture represents the predominant rupture form in the process of rock collapse, with the potential to cause significant disruption. Consequently, there is a clear need to study the acoustic emission signal characteristics and strain evolution law in the process of rock tension damage. This will facilitate the identification of precursor information of rock damage, which can then be used for the monitoring and early warning of rock collapse. [Methods] A three-point bending test(1.0 m×0.5 m×0.15 m) was conducted on a large-scale tuff to monitor the rupture process in real time using acoustic emission technology and digital image correlation technology. The acoustic emission signals and deformation evolution characteristics of large-scale tuff in the process of tensile damage were then analyzed by combining principal component analysis and hierarchical clustering algorithms. [Results] The application of acoustic emission detection technology allows for the accurate identification of the damage state of the rock mass, as well as the provision of effective damage precursor information. [Conclusion] The result show that:(1) According to the evolution of acoustic signal parameters, the tensile failure of rock can be divided into four stages: microcrack initiation stage, small-scale and stable cracking stage, unstable cracking stage and failure stage;(2) The acoustic signals can be divided into six categories using the acoustic emission parameters using clustering and principal component analysis algorithm. The cumulative changes and proportions of the two types of characteristic signals, the gradual low-amplitude type signal and the sudden high-amplitude type signal, are capable to characterize the cracking process;(3) The findings indicate that the acoustic emission characteristic parameters and their evolution characteristics can effectively reflect the rock rupture process, providing precursor information for rock damage up to 141 seconds in advance compared with the simultaneous deformation monitoring. The result of this study offer insights that can inform the development of effective method and techniques for monitoring and early warning of rock failure. This research could provide support for rock collapse monitoring and early warning.

关键词

声发射 / 机器学习 / 数字图像相关 / 破坏前兆 / 岩体崩塌 / 变形 / 影响因素

Key words

acoustic emission / machine learning / digital image correlation / failure precursor / rock collapse / deformation / influencing factors

基金资助

国家自然科学基金项目(4216020116)

贵州省科学技术基金项目([2020]1Y185)