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<p>Laboratory calibration of acoustic emission (AE) behaviour in active waveguide system (AWS) is crucial for AE-based landslide monitoring. This study proposes a novel strategy to analyse the AE signal parameters generated during AWS deformations, aimed at predicting landslide velocity scales corresponding to distinct slope instability states. Compression tests were conducted on models of AWS using a universal testing machine to simulate strain-induced interactions within backfill material, thereby generating AE signals. Deformation rates ranged from extremely slow (0.003&#xa0;mm/min) to rapid (30.0&#xa0;mm/min), incorporating two intermediate scales (0.03 and 3.0&#xa0;mm/min) within the Varnes’ landslide velocity framework. Key AE signal parameters including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives, were systematically analyzed for each velocity scale. A strong proportional relationship was observed between cumulative AE counts and deformation rate, while signal strength exhibited a quadratic correlation with amplitude. AE activity or order of AE signals per unit time, corresponding to each velocity scale were also analyzed. Significant results and correlations were verified using a different model of AWS. Additionally, a single test consisting of all the velocity scales in sequential order was conducted on the AWS and results were in consistence. The findings offer valuable insights for developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS.</p>

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Assessing landslide velocity scales with acoustic emission active waveguides for early warning system

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Deepak Kumar,
  • Ajit K. Mahapatro,
  • Sushil Kumar Singh

摘要

Laboratory calibration of acoustic emission (AE) behaviour in active waveguide system (AWS) is crucial for AE-based landslide monitoring. This study proposes a novel strategy to analyse the AE signal parameters generated during AWS deformations, aimed at predicting landslide velocity scales corresponding to distinct slope instability states. Compression tests were conducted on models of AWS using a universal testing machine to simulate strain-induced interactions within backfill material, thereby generating AE signals. Deformation rates ranged from extremely slow (0.003 mm/min) to rapid (30.0 mm/min), incorporating two intermediate scales (0.03 and 3.0 mm/min) within the Varnes’ landslide velocity framework. Key AE signal parameters including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives, were systematically analyzed for each velocity scale. A strong proportional relationship was observed between cumulative AE counts and deformation rate, while signal strength exhibited a quadratic correlation with amplitude. AE activity or order of AE signals per unit time, corresponding to each velocity scale were also analyzed. Significant results and correlations were verified using a different model of AWS. Additionally, a single test consisting of all the velocity scales in sequential order was conducted on the AWS and results were in consistence. The findings offer valuable insights for developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS.