## 1 Introduction  
Helical anchors have a century-old application history in engineering practice [1, 2] and have the potential to be used to support offshore floating structures [3] due to their advantages of rapid installation and high uplift capacity. A typical helical anchor comprises a central steel shaft and several helical steel plates welded onto the shaft, as shown in Figure 1. In designing floating structures, one needs to accurately predict the monotonic tensile capacity of the helical anchor, which is affected by the relative density of the soil, the embedment ratio of the helices, the soil friction angle, the dilation angle, the number of helices, the installation effect, and many other factors; the monotonic tensile capacity of the helical anchor is difficult to predict. The uplift capacity of a helical anchor in sand is generally predicted through experiments, including field tests [4; 5], small-scale model tests [6; 7; 8; 9; 10; 11; 12; 13], and centrifuge tests [14; 10; 15; 16; 17; 18]. Experimental studies are relatively costly and time-consuming, especially field tests and centrifuge tests; additionally, it is challenging to obtain reliable data due to the complex operating environment. For small-scale studies, the experimental parameters are relatively more controllable; however, the size effect hinders the prediction accuracy. Thus, making full use of existing and reliable experimental results is of great significance for accurately predicting the uplift capacity of helical anchors in sand. Given the advanced capabilities of AI technologies, they have been applied to a variety of related problems in civil engineering, such as predicting the bearing capacity of monopiles [19; 20] and pile settlements [21], and determining the frictional resistance of driven piles [22]. Specifically, [PERSON] et al. used multiobjective evolutionary polynomial regression to identify strong correlations between factors; their study highlights the influence of undrained cohesion and effective stress on adhesion and provides a guarantee for optimized bored pile designs under undrained conditions [23]. Furthermore, a novel model that predicts the friction capacity of driven piles was proposed based on a multiobjective genetic algorithm for evolutionary polynomial regression [24]. [PERSON] et al. constructed a Bayesian neural network model to estimate the adhesion factor of undrained side resistance [25]. In addition, [PERSON] and [PERSON] compared backpropagation neural networks and multiple adaptive regression splines to explore pile drivability [26]. [PERSON]  
Figure 1: Helical anchor definitions. et al. established a variety of nonlinear models to estimate the ultimate bearing capacity of shallow footing under double-layer soil conditions [27]. Furthermore, [PERSON] and [PERSON] [28] proposed an AI-technology-based model to predict the uplift capacity of helical piles in sand using 41 1\(\times\)\(g\) model tests. However, the above studies have at least three shortcomings: (1) the application of AI in geotechnical engineering problems is mostly based on artificial neural networks (ANN), but the \"black box\" nature of these models leads to poor interpretability [29]; (2) the feasibility of using other advanced models, such as tree-based ensemble algorithms, to predict the monotone tensile capacity of helical anchors in sand is still unknown; and (3) the scale effect, which significantly influences the strength of sand [30], was not considered in previous research [28]. To address these issues, in this study, the results of centrifugation tests were used to prepare the dataset, and a more robust AI technique, the gradient-boosting decision tree (GBDT) approach, was used to predict the monotonic tensile capacity of helical anchors in sand. GBDT is an ensemble learning technique that can improve the predictive accuracy by combining many decision tree models [31].