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<p>Several established methods exist for designing geosynthetic reinforced pile-supported embankments (GRPSE), each based on different assumptions and theoretical frameworks. As a result, their predictions can vary, particularly when applied to sites with variable soil conditions. In this study, we carried out a detailed probabilistic assessment to compare four widely adopted design methods, BS8006, EBGEO, CUR226, and NGG, with a three-dimensional (3D) unit-cell finite element (FE) model. The FE model was first validated using data from a well-documented field study and then used, along with the analytical methods, to assess the influence of soil variability and change in geometry on system behavior. Both deterministic and probabilistic analyses were conducted across a range of embankment heights, pile spacings, and pile diameters to capture performance under typical and extreme configurations. The results show that GRPSE performance is strongly influenced by soil variability, with the greatest sensitivity observed in deformation-related parameters such as differential settlement and geosynthetic mobilized tensile force. These effects are amplified when the structure operates closer to its design limits. While all design methods reproduced the overall stress transfer trends reasonably well, their deformation estimates tended to differ. BS8006 and EBGEO generally yielded higher values in both deterministic and probabilistic analyses, reflecting their conservative nature. CUR226, although slightly conservative, showed the closest agreement with the FE model and captured the influence of soil variability and geometry more consistently. Compared to the analytical approaches, the FE model produced more stable responses under uncertainty, indicating its greater ability to represent soil-structure interaction across varied conditions. These findings highlight the importance of incorporating soil uncertainty into GRPSE design frameworks and integrating analytical insight with numerical modeling to achieve more reliable and efficient solutions in the future.</p>

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Probabilistic Performance Assessment of Geosynthetic Reinforced Pile-Supported Embankments Using Design Methods and 3D Finite Element Modeling

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Ekansh Agarwal,
  • Ning Luo

摘要

Several established methods exist for designing geosynthetic reinforced pile-supported embankments (GRPSE), each based on different assumptions and theoretical frameworks. As a result, their predictions can vary, particularly when applied to sites with variable soil conditions. In this study, we carried out a detailed probabilistic assessment to compare four widely adopted design methods, BS8006, EBGEO, CUR226, and NGG, with a three-dimensional (3D) unit-cell finite element (FE) model. The FE model was first validated using data from a well-documented field study and then used, along with the analytical methods, to assess the influence of soil variability and change in geometry on system behavior. Both deterministic and probabilistic analyses were conducted across a range of embankment heights, pile spacings, and pile diameters to capture performance under typical and extreme configurations. The results show that GRPSE performance is strongly influenced by soil variability, with the greatest sensitivity observed in deformation-related parameters such as differential settlement and geosynthetic mobilized tensile force. These effects are amplified when the structure operates closer to its design limits. While all design methods reproduced the overall stress transfer trends reasonably well, their deformation estimates tended to differ. BS8006 and EBGEO generally yielded higher values in both deterministic and probabilistic analyses, reflecting their conservative nature. CUR226, although slightly conservative, showed the closest agreement with the FE model and captured the influence of soil variability and geometry more consistently. Compared to the analytical approaches, the FE model produced more stable responses under uncertainty, indicating its greater ability to represent soil-structure interaction across varied conditions. These findings highlight the importance of incorporating soil uncertainty into GRPSE design frameworks and integrating analytical insight with numerical modeling to achieve more reliable and efficient solutions in the future.