Seismic bearing capacity and fragility analysis of geogrid-encased stone column composite foundations

Geogrid-encased stone columns (GESCs) have shown notable potential in improving performance, thereby reducing the seismic failure probability ( P F ) of soil. This research proposes a limit equilibrium-based formulation for predicting the ultimate seismic bearing capacity ( q u ) of GESC composite foundations. Subsequently, a fragility analysis framework is developed based on the bearing capacity formula to quantify P F . The fragility analysis incorporates machine learning to evaluate the influence of tensile strength ( T ), column diameter ( D c ), column and soil strength parameters ( φ c , c c , φ s , and c s ), shear strength utilization ratio ( n ), area replacement ratio ( m ), vertical load demand ( P v ), footing width ( B ), footing embedded depth ( h 0 ), and seismic coefficients ( k h and k v ). Results demonstrate that encasement substantially enhances q u and reduces earthquake-induced settlements. The fragility function demonstrates a critical behavioral transition at n ≈ 0.5. P F decreases with increasing T , m , φ c , c c , φ s , c s , B , and h 0 , but increases with k v , D c , and P v . The significant impact and indeterminacy of soil properties suggest P F shall be reduced by selecting the controllable parameters (e.g., T and D c ). Larger B improves load diffusion, and increased h 0 maximizes vertical effective stress. Large D c delays confinement mobilization, potentially reducing the reinforcement effectiveness and increasing the failure risk. • A limit equilibrium-based formulation for calculating ultimate seismic bearing capacity of GESC. • A machine learning-based fragility analysis framework for quantifying the failure probability of GESC. • GESC withstands a high acceleration level of 0.9 g, while OSC fails at 0.54 g. • Controllable parameters shall be considered due to the significant effect of surrounding soil on failure probability.