## 1 Introduction  
The study of the mechanical properties of the soil-structure interface is crucial for understanding the interaction between soil and underground engineering structures, making it a hot topic in geotechnical engineering [1, 2, 3]. In recent years, the artificial freezing method has been widely used in underground engineering construction, such as coal mine shafts, subway connecting passages, and tunnel shield originating or receiving. Consequently, the physical and mechanical properties of the frozen-thawed soil-structure interface have attracted increasing attention from researchers [4, 5, 6, 7]. Understanding the shear behavior of the frozen-thawed soil and structure is of great significance in artificial freezing engineering design and construction. Several researchers have explored and identified significant differences in mechanical behavior between frozen soil and other soil types. In the realm of laboratory testing, [PERSON] et al. studied the shear stress-strain relationship and strength characteristics of the Qinghai-Tibet silt-concrete interface through direct shear tests under varying moisture content and temperature conditions [8]. [PERSON] et al. developed a multifunctional cyclic direct shear system for the frozen soil-structure interface and conducted shear tests under different boundary conditions [9]. Theoretical models have also been advanced. [PERSON] et al. established a Duncan-Chang constitutive model describing the stress-strain propertiesof frozen loess [10]. [PERSON] proposed a new twin-shear strength criterion, elucidating the relationship among single-shear, twin-shear, and triple-shear strength criteria [11]. These achievements have significantly advanced the understanding of the frozen soil-structural interface. However, it should be noted that in deep underground construction using the artificial freezing method, stress in the soil increases with depth, and frost heave force is also significant under freezing conditions. Under such high stress conditions, the mechanical properties of the frozen soil-structure interface will differ significantly from those formed under conventional stress conditions. Recently, with the development of big data mining technology and machine learning (ML) algorithms, researchers have introduced these techniques into the study of frozen soil. For instance, evolutionary polynomial regression has been used to establish the constitutive function of frozen soil [12], and artificial neural networks (ANNs) have been employed to assess soil thermal conduction [13] and to estimate various soil samples under freeze-thaw conditions [14]. The mechanical behavior of the frozen soil-structure interface is complex and difficult to describe using ordinary mathematical models [15]. Intelligent ML algorithms provide a reasonable, fast, and rigorous solution to this issue. Moreover, establishing ML models based on existing historical data is more economical than conducting laboratory measurements each time to study the mechanical behavior of the frozen soil-structure interface [2; 16]. The modeling of frozen-thawed soil-structure interface shear behaviors using machine learning based on extensive indoor shear test data is limited, which deserves further exploration. In this paper, test data obtained from high normal stress and freeze-thaw direct shear tests were used to train and validate an integrated CART-based ML model. The CART algorithm is particularly suitable for small datasets and has high tolerance for data distribution. It demonstrates that the CART-based ML model has a better ability to describe the shear stress-strain relationship and strain softening phenomenon of the frozen-thawed soil-structure interface. This paper is organized as follows: Section 2 introduces the high-stress frozen-thawed soil-structure shear test. Section 3 details the CART algorithm used in the study. Section 4 describes the process of establishing the CART-based ML model.