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Granular Scaling Approach to Landslide Runout.

Rory T Cerbus1,2, Ludovic Brivady2, Thierry Faug3

  • 1RIKEN Center for Biosystems Dynamics Research (BDR), 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan.

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|July 12, 2024
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Summary
This summary is machine-generated.

Predicting landslide runout distance is crucial. This study simplifies prediction by linking fall height and grain size distribution to landslide mobility, uniting lab and field data.

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Area of Science:

  • Geosciences
  • Geotechnical Engineering
  • Natural Hazards

Background:

  • Landslide research aims to predict travel distance, a complex phenomenon.
  • Numerous parameters influence landslide behavior, complicating accurate predictions.

Purpose of the Study:

  • To simplify landslide runout prediction.
  • To investigate the relationship between fall height, grain size distribution, and landslide mobility.

Main Methods:

  • Granular laboratory experiments were conducted.
  • A scaling analysis was performed on experimental and field data.

Main Results:

  • Accounting for fall height and grain size distribution improved normalized runout correlation.
  • Laboratory and field data were quantitatively unified.
  • Landslide mobility increases with the square root of fall height.
  • Landslide mobility increases with the skewness of the grain size distribution.

Conclusions:

  • Fall height and grain size distribution are key parameters for predicting landslide runout.
  • The developed model provides a unified approach for laboratory and field landslide data.
  • The findings offer a simplified yet effective method for landslide mobility assessment.