Helical anchors are widely used in engineering to resist tension, especially during offshore wind energy harvesting, and their uplift behavior in sand is influenced by many factors. Experimental studies are often used to investigate these anchors; however, scale effects are inevitable in 1× g model tests, soil conditions for in situ tests are challenging to control, and centrifuge tests are expensive and rare. To make full use of the limited valid data and to gain more knowledge about the uplift behaviors of helical anchors in sand, a prediction model integrating gradient-boosting decision trees (GBDT) and particle swarm optimization (PSO) was proposed in this study. Data obtained from a series of centrifuge tests formed the dataset of the prediction model. The relative density of soil, embedment ratio, helix spacing ratio, and the number of helices were used as input parameters, while the anchor mobilization distance and the ultimate monotonic uplift resistance were set as output parameters. A GBDT algorithm was used to construct the model, and a PSO algorithm was used for hyperparameter tuning. The results show that the optimal GBDT model accurately predicted the anchor mobilization distance and the ultimate monotonic uplift resistance of helical anchors in dense fine silica sand. By analyzing the relative importance of influencing variables, the embedment ratio was found to be the most significant variable in the model, while the relative density of the fine silica sand soil, the helix spacing ratio, and the number of helices had relatively minor influence. In particular, the helix spacing ratio was found to have no influence on the capacity of adjacent helices when S/D > 6.

Helical piles with small sizes are widely used in traditional civil engineering infrastructure application, especially in onshore electrical power industry. The floating offshore wind power industry has taken an interest in helical piles because of their rapid installation and ability to support immediate loading. Compared with onshore structures, wind power structures suffer larger external load and it becomes necessary to increase the overall size of helical piles, leading to considerable load capacity provided by the shaft, which is typically neglected for a light structure. In this paper, the undrained uplift capacity of helical piles was studied using finite-element limit analysis. The analysis explored various diameter ratios of the shaft to the plate and embedment depths, as well as different interface conditions. The local bearing capacity on the plate was also explored for attached and vented interface conditions. The failure mechanisms of a helical pile were presented to explain the mutual influence between plate anchor and shaft on the uplift capacity. Algebraic expressions for the uplift capacity of a helical pile were proposed. Engineers may use these expressions to improve the accuracy of their estimates for the uplift capacity of helical piles.

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 (θ), strength ratio (s_uc/γB), heterogeneous index (ρB/s_uc), and anisotropy ratio (r_e). Here, s_uc represents the soil strength obtained from triaxial compression tests, while ρ denotes the strength gradient. The results indicate that the bearing capacity increases with the increase in L/B, s_uc/γB, ρB/s_uc, and r_e, while the maximum bearing capacity corresponds to a load angle ranging from 75° to 90°. 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 θ 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.