This study provides a comprehensive analysis of the undrained failure envelope for spudcan foundations in anisotropic clays using the AUS failure criterion as the soil strength model. The influence of embedment depth (L/D) and anisotropic strength (re) on spudcan behaviour under combined loading conditions is investigated. Failure envelopes are derived through three-dimensional finite element limit analysis (3D FELA) in both (H/ suTCA, M/suTCAD) and (V/Vult, H/suTCA, M/suTCAD) spaces. The study also illustrates spudcan foundation failure mechanisms, providing valuable insights for designing footings in anisotropic clays under combined loads (V, H, M). Additionally, an innovative soft-computing approach is introduced: a machine learning model that integrates categorical boosting (CatBoost) with the flower pollination algorithm (FPA) for optimized predictions of the spudcan failure envelope. The proposed FPA-CatBoost model is validated against numerical FELA results, demonstrating a strong correlation and offering engineers a reliable tool for determining spudcan foundation failure envelopes under varied loading conditions.

The development of marine energy requires reliable foundations, which may be located near submarine slopes. This paper utilizes the lower bound limit analysis (LBLA) to analyze the undrained bearing capacity of foundations on slopes with anisotropy and linearly increasing strength with depth. The anisotropic undrained strength (AUS) model is employed to simulate the anisotropy of the slope soil. This study considers five variables that affect the bearing capacity: the normalized foundation setback (L/B), load angle (theta), strength ratio (suc/gamma B), heterogeneous index (rho B/suc), and anisotropy ratio (re). Here, suc represents the soil strength obtained from triaxial compression tests, while rho denotes the strength gradient. The results indicate that the bearing capacity increases with the increase in L/B, suc/gamma B, rho B/suc, and re, while the maximum bearing capacity corresponds to a load angle ranging from 75 degrees to 90 degrees. The failure modes of foundations under different boundary conditions were presented and discussed. To establish the relationship between the foundation bearing capacity and each variable, the multivariate adaptive regression splines (MARS) is introduced. The MARS results indicate that theta is the most significant variable, while the relative importance of L/B is the lowest; neither can be neglected in practical engineering. The empirical equation based on the MARS algorithm can accurately predict the bearing capacity of foundations in non-homogeneous and anisotropic clay. These results offer critical guidance for engineering practice, enabling efficient design of marine foundations near slopes while accounting for soil anisotropy and heterogeneous strength gradients, thereby reducing risks of instability in offshore energy infrastructure.

The fundamental issue of bearing capacity of footings on anisotropic clays holds significant importance in geotechnical engineering. Previous investigations predominantly focused on deterministic analyses, disregarding the spatial variability of soil. A probabilistic analysis of the bearing capacity of footings is conducted in this paper, incorporating the spatial variability of anisotropic clays. To achieve this, Random Adaptive Finite Element Limit Analysis (RAFELA) and Monte Carlo simulations are utilised to capture the full spectrum of potential outcomes under parametric uncertainty. The impact of anisotropic soil strength variability is explored across three input parameters such as the anisotropic strength ratios, coefficients of variation, and dimensionless correlation lengths. In order to establish surrogate models capable of predicting random bearing capacity of anisotropic clays, Artificial Neural Network (ANN) models are developed. The use of the proposed ANN surrogate models presents a more convenient and computationally efficient approach for predicting the ultimate vertical load of footings on spatially random anisotropic clays.

In underground space technology, the issue of tunnel stability is a fundamental concern that significantly causes catastrophe. Owing to sedimentation and deposition processes, the strengths of clays are anisotropic, where the magnitudes of undrained shear strengths in the vertical and horizontal directions are different. The anisotropic undrained shear (AUS) model is effective at considering the anisotropy of clayey soils when analyzing geotechnical stability issues. This study aims to assess the stability of rectangular tunnels by adjusting the dimensionless overburden factor, cover-depth ratio, and width-depth ratio in clay with various anisotropic strength ratios. The stability analysis of these tunnels involves employing finite element limit analysis and the AUS model to identify the planes of soil collapse in response to the aforementioned variations. In addition, this study presents the development of soft-computing models utilizing artificial neural networks (ANNs) to forecast the stability of rectangular tunnels across various combinations of input parameters. The findings of this study are presented in the form of design charts, tables, and soft-computing models to facilitate practical applications.