Probabilistic Analysis of the Seismic Bearing Capacity of Strip Footings Using RAFELA and MARS

The critical aspect of the seismic bearing capacity of footings holds significant importance in the field of geotechnical engineering. Past research has primarily focused on deterministic analyses, mainly neglecting or ignoring the spatial variability of the soil. This study aims to address this gap by employing a probabilistic approach to assess the seismic bearing capacity of foundations while considering the seismic force effect by adopting the pseudo-static approach. To achieve this goal, this study utilizes the random adaptive finite element limit analysis technique and Monte Carlo simulations to cover a wide range of potential outcomes, taking into account the uncertainties in the parameters. This research investigated the influence of soil strength variability on three key factors: the horizontal seismic coefficient, coefficient of variation, and dimensionless correlation length. The study revealed that an increase in the coefficient of variation of the undrained shear strength (COVsu) and the dimensionless correlation length (Theta su) leads to a reduction in the mean of the random seismic bearing capacity factor (mu Nran). Conversely, the horizontal seismic coefficient (kh) negatively impacts the seismic bearing capacity, thereby diminishing the overall soil stability. Additionally, the factor of safety must be selected with caution to ensure that the probability of failure is less than a specified value, particularly when the coefficient of variation of the undrained shear strength (COVsu) is high. To establish surrogate models capable of predicting the random seismic bearing capacity, multivariate adaptive regression spline (MARS) models have been developed. Utilizing the proposed MARS surrogate models offers a more convenient and computationally efficient means of evaluating the impact of variability in soil strength properties on geotechnical stability calculations.