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<p>This study investigates the relationship between surface crack development and excess pore water pressure (EPWP) during rainfall-induced debris flow conditions. A custom-designed tilting flume integrated with a rainfall simulator was fabricated to replicate slope failure scenarios. Silty sand was tested under controlled conditions on 45° and 60° slopes with identical rainfall intensities. Surface cracks were quantified using an image processing algorithm to calculate crack percentages, and real-time EPWP measurements were recorded to assess their correlation. The results demonstrate that surface crack formation significantly influences EPWP, suggesting a potential interdependence between these parameters. Furthermore, the study evaluates whether EPWP can serve as an effective threshold parameter for landslide early warning systems (LEWS). These findings contribute to a better understanding of landslide mechanics and provide critical insights for enhancing LEWS design and implementation.</p>

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Rainfall-Induced Slope Instability in a Tilting Flume: Analysis of Pore Pressure Variations and Surface Crack Percentage

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Varun Menon,
  • Sreevalsa Kolathayar

摘要

This study investigates the relationship between surface crack development and excess pore water pressure (EPWP) during rainfall-induced debris flow conditions. A custom-designed tilting flume integrated with a rainfall simulator was fabricated to replicate slope failure scenarios. Silty sand was tested under controlled conditions on 45° and 60° slopes with identical rainfall intensities. Surface cracks were quantified using an image processing algorithm to calculate crack percentages, and real-time EPWP measurements were recorded to assess their correlation. The results demonstrate that surface crack formation significantly influences EPWP, suggesting a potential interdependence between these parameters. Furthermore, the study evaluates whether EPWP can serve as an effective threshold parameter for landslide early warning systems (LEWS). These findings contribute to a better understanding of landslide mechanics and provide critical insights for enhancing LEWS design and implementation.