ISSN 1003-8035 CN 11-2852/P

    松散堆积体斜坡变形-滑移过程的声发射特征参数演化规律

    Evolution of the characteristic parameters of acoustic emission from deformation to failure of a loose soil slope

    • 摘要: 松散堆积体在自然界和工业生产中广泛存在,有复杂的力学性质和相对较高的失稳风险。为研究其在斜坡变形中的滑移失稳过程,基于声发射技术探究了松散体从静止、蠕变到滑移整个过程中声学特征演化规律。分析松散体在滑移过程中的声发射特征(acoustic emission,AE)参数,然后结合松散体的状态变化对AE演化阶段进行了划分,最后结合颗粒图像测速法(particle image velocimetry,PIV)分析和频谱变化进一步验证了松散体滑移过程的AE演化规律。结果表明:振铃计数和能量随滑移过程而逐渐增大,b值(小事件数与大事件数的比值)随滑移过程逐渐降低,b值、振铃计数和能量的滑移门槛值为0.2、5000次和1500 mV·ms,其中b值对松散体的状态变化更敏感;频谱重心在临滑移前期有30~50 kHz的降幅,而后发生震荡变化,其震荡时间区域正好对应振铃计数和能量数值相对较高及b值相对较低的时间区域;此外松散体滑移前存在一个重要的“窗口期”,说明AE技术具有识别松散体滑坡前兆的潜力。

       

      Abstract: Loose accumulations are widely present in nature and industrial production, possessing complex mechanical properties and relatively high risks of instability. To investigate their sliding instability process during slope deformation, this study explores the evolution of acoustic characteristics throughout the entire process of loose accumulations, from static state, creep to sliding, based on acoustic emission (AE) technology. The AE characteristic parameters of loose accumulations during the sliding process are analyzed, and the AE evolution stages are divided based on the state changes of loose accumulations. Finally, the AE evolution law of the sliding process of loose accumulations is further verified through particle image velocimetry (PIV) analysis and spectral changes. The results indicate that the ringing count and energy gradually increase with the sliding process, while the b-value gradually decreases. The sliding threshold values for the b-value, ringing count, and energy are 0.2, 5000 counts, and 1500 mV·ms, respectively, with the b-value being more sensitive to state changes in loose accumulations. The spectral centroid experiences a decrease of 30~50 kHz in the early pre-sliding stage, followed by oscillatory changes. The time period of these oscillations corresponds to relatively high ringing count and energy values and relatively low b-values. Furthermore, there is an important “window period” before the sliding of loose accumulations, indicating that AE technology has the potential to identify precursors of loose accumulation landslides.

       

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