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<p>Loess collapse is highly concealed and abrupt. Consequently, traditional displacement monitoring is ineffective in capturing early instability signals and disaster warning timeliness is poor, a key challenge in loess disaster prevention. Hence, this paper focused on nonforce field monitoring and uses acoustic emission (AE) technology to conduct uniaxial compression tests on Malan loess. Tests explored AE responses during loess deformation/failure, analyzed AE parameter anomalies near failure, and established precursor criteria. Results show distinct AE signals throughout the process are strongly correlated with failure stages. Near peak strength, the AE ringing count rises rapidly and peaks before specimen failure, which confirms AE anomalies precede macroscopic strain changes. Before peak strength, the AE b-value drops abruptly, high-frequency signals disappear, and low-frequency signals surge. This paper identified three reliable AE-based failure precursors: sudden increase in AE ringing count, abrupt b-value drop, and “high-frequency disappearance–low-frequency surge.” This paper confirmed AE monitoring’s feasibility for loess collapse early warning and provides a technical basis to enhance disaster prevention efficiency and protect infrastructure/ecology in loess regions.</p>

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Study on acoustic emission characteristics and failure precursors of Malan loess under uniaxial compression

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Xiaohui Sun,
  • Yanrong Li,
  • Zhibin Wang,
  • Jianbo Zhao,
  • Xiaoyu Yang,
  • Ke Zhang

摘要

Loess collapse is highly concealed and abrupt. Consequently, traditional displacement monitoring is ineffective in capturing early instability signals and disaster warning timeliness is poor, a key challenge in loess disaster prevention. Hence, this paper focused on nonforce field monitoring and uses acoustic emission (AE) technology to conduct uniaxial compression tests on Malan loess. Tests explored AE responses during loess deformation/failure, analyzed AE parameter anomalies near failure, and established precursor criteria. Results show distinct AE signals throughout the process are strongly correlated with failure stages. Near peak strength, the AE ringing count rises rapidly and peaks before specimen failure, which confirms AE anomalies precede macroscopic strain changes. Before peak strength, the AE b-value drops abruptly, high-frequency signals disappear, and low-frequency signals surge. This paper identified three reliable AE-based failure precursors: sudden increase in AE ringing count, abrupt b-value drop, and “high-frequency disappearance–low-frequency surge.” This paper confirmed AE monitoring’s feasibility for loess collapse early warning and provides a technical basis to enhance disaster prevention efficiency and protect infrastructure/ecology in loess regions.