Various research methods have been utilized to investigate surface subsidence resulting from shield tunnel construction. These include theoretical analysis methods [4; 5], numerical simulation methods [6; 7], and empirical formula methods [8; 9; 10]. [PERSON] and [PERSON] [11] proposed that artificial neural networks could be an effective tool for predicting ground subsidence, as they enable the combination of extensive computer databases with knowledge of ground subsidence effects. [PERSON] et al. [12] utilized numerical simulation software to investigate the suitability of different soil transients for assessing long-term subsidence caused by shield tunneling. [PERSON] et al. [13] suggested a superposition method to estimate 3D ground displacement resulting from shield machine excavation. [PERSON] et al. [14] developed an equation for predicting the final surface settlement distribution based on a Gaussian curve and demonstrated the effect of tunnel depth and ground loss on longitudinal surface settlement induced by tunnel construction using an indoor model test. Recently, scholars have employed artificial intelligence methods such as neural networks and deep learning to predict ground subsidence caused by shield tunneling. For example, [PERSON] et al. [15] utilized a deep learning approach to predict ground response due to tunneling in karst ground, while [PERSON] et al. [16] proposed an artificial intelligence method to predict land subsidence during shield tunneling by considering the interaction between multiple factors. Numerous methods have surfaced in the continual advancement of subsidence prediction research. Nevertheless, it is necessary to recognize that the construction setting of shield tunnels has grown progressively intricate, and each undertaking showcases singular traits. Such distinct engineering attributes may give rise to variations in surface subsidence, consequently influencing the mechanical response of the structure. This interdependent impact may engender inconsistencies in the outcomes of the initial prediction approaches. Thus, the adaptation of the original subsidence prediction methods to the specific engineering features is imperative. The consequences of shield tunnel excavation on adjacent foundation pits reveal significant differences in the settlement patterns of soil at the bottom of the foundation pit compared to that of a conventional tunnel excavation. This issue has been thoroughly examined by many researchers, such as [PERSON] et al. [17], [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22]. [PERSON] et al. [23] explored the lateral force exerted on adjacent shield tunnels induced by excavations, while [PERSON] et al. [24] assessed the influence of excavation on the deformation of lower shield tunnels and simulated the efficacy of different reinforcement and control methods. In addition, [PERSON] et al. [25] proposed a novel simplified analysis method for predicting the longitudinal deformation of extant subway tunnels provoked by adjacent excavations. To further elaborate, compared to projects where tunnels only pass underneath ordinary foundation pits, the deformation pattern of the soil at the bottom of deep foundation pits with pile-raft foundations that are crossed by the shield of a super-large shield tunneling machine will differ significantly from the ground settlement pattern caused by traditional shield tunnel construction. At present, limited research exists regarding the intersection of large-diameter shield tunnels and deep foundation pits outfitted with pile-raft foundations. The alterations in the adjacent soil within this type of engineering endeavor often deviate from the forecasted results of conventional approaches. These disparities not only affect surface settlement, they impact the mechanical reaction of the structure. In the case of pile-raft foundations, the bearing capacity of the friction piles is most directly impacted, which is intimately connected to soil settlement and deformation.