Probabilistic Assessment on the Performance of Tiered Geosynthetic Reinforced Soil Walls Using RLEM and RFEM

Nazari, Reza A.; Sharafi, Hassan; Jafari, Habib

doi:10.1007/s40891-022-00418-7

Cited by 2 publications

(1 citation statement)

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“…According to the results, suction stress plays a more signi cant role in determining the required normalized reinforcement than tensile strength cut-offs. Nazari et al [27] examined the probabilistic stability of tiered GRS walls under seismic loads, taking into account soil spatial variability and cross-correlation between input variables. The critical slip surface was identi ed using random limit equilibrium methods (RLEM).…”

Section: Introductionmentioning

confidence: 99%

Stochastic model predictive approach for seismic lateral displacement of geosynthetic- reinforced soil slopes based on Newmark's sliding block analysis

Ghanbari,

Nazari,

Sharafi

2024

Preprint

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As a result of technological advances, geosynthetic reinforced soil (GRS) slopes have been constructed more frequently in recent decades, which have been used extensively in landslide stabilization, highway construction, and geological disaster prevention. A new formula was developed for predicting the probabilistic sliding displacement of GRS slopes in this paper. An example of a model based on numerical simulation (Slide) was illustrated to calculate seismic displacement based on three types of Newmark analysis. A synthetic dataset including 972 numerical simulations was generated for statistical analysis by data derived from real-time strong-ground motions of 30 worldwide earthquakes. An investigation into the relationship between reinforced slope properties and motion characteristics was performed using a parametric analysis. It was concluded that coupled analysis calculated higher values for earthquake-induced sliding displacement of GRS slope. Also, statistical analysis indicated that soil friction angle is more influential on sliding displacement than the other random variables. A cumulative distribution function was constructed for estimating probabilistic seismic displacement based on 5000 Latin-hypercube sampling.

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Section: Introductionmentioning

confidence: 99%