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<p>This research presents an enhanced probabilistic design framework of unsaturated soil slopes, accounting for spatial variability in shear strength and soil water retention curve (SWRC) parameters. An efficient methodology is proposed, utilizing the Cholesky decomposition technique to generate random fields for design parameters. The variability in shear strength and SWRC model parameters is incorporated in estimating the second-order reliability index for spatially variable unsaturated finite clayey slopes. Five autocorrelation functions (ACFs) are analyzed to determine the most conservative ACF for optimal finite clayey slope design. Results indicate that the most frequently employed single exponential autocorrelation function (ACF) underestimates the failure probability. Furthermore, the failure probability significantly increases with greater horizontal and vertical autocorrelation distances (ACDs), leading to more homogeneous random fields for the design parameters. ACDs significantly affect the center of rotation and critical slip surfaces (CSS). The developed design charts offer practical tools for engineers, supporting more resilient and sustainable slope designs. This methodology bridges the gap between unsaturated soil mechanics and engineering practice.</p>

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Probabilistic Design of Unsaturated Clay Slopes with Spatial Variability of Shear Strength and SWRC Parameters

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Ammavajjala Sesha Sai Raghuram,
  • B Munwar Basha

摘要

This research presents an enhanced probabilistic design framework of unsaturated soil slopes, accounting for spatial variability in shear strength and soil water retention curve (SWRC) parameters. An efficient methodology is proposed, utilizing the Cholesky decomposition technique to generate random fields for design parameters. The variability in shear strength and SWRC model parameters is incorporated in estimating the second-order reliability index for spatially variable unsaturated finite clayey slopes. Five autocorrelation functions (ACFs) are analyzed to determine the most conservative ACF for optimal finite clayey slope design. Results indicate that the most frequently employed single exponential autocorrelation function (ACF) underestimates the failure probability. Furthermore, the failure probability significantly increases with greater horizontal and vertical autocorrelation distances (ACDs), leading to more homogeneous random fields for the design parameters. ACDs significantly affect the center of rotation and critical slip surfaces (CSS). The developed design charts offer practical tools for engineers, supporting more resilient and sustainable slope designs. This methodology bridges the gap between unsaturated soil mechanics and engineering practice.