Probabilistic design optimization of rectangular skirted mudmats under combined loading in spatially variable soil

Natural soil exhibits inherent spatial variability due to complex physical and chemical formation processes, making it necessary to analyze, evaluation and design foundations using probabilistic method. In this study, the bearing capacity of rectangular skirted mudmats in spatially variable soil is investigated by three-dimensional random finite element analysis (3D-RFEA). Given the high computational cost of 3D-RFEA, an active learning probabilistic method based on Kriging surrogate model is proposed to predict the mean and coefficient of variation of bearing capacity factor of mudmats. Then, considering the effects of the skirt depth ratio of mudmats and the variation coefficient and scales of fluctuation of the undrained shear strength of soil, a prediction model of probabilistic failure envelope is proposed by combining the normalized deterministic failure envelope and the cumulative distribution function (CDF) of the uniaxial bearing capacity factor. Based on the prediction model of probabilistic failure envelope, an example of probabilistic design optimization of mudmats is finally given. Compared with the normative method, the design method based on the prediction model can design mudmats directly with the permissible failure probability.