## 1 Introduction  
In recent years, the development of three-dimensional urban transportation has made conducting excavations above existing shield tunnels inevitable [1; 2; 3; 4; 5]. During the construction of such excavations, the surrounding soil experiences disturbance, stress release, and uplift at the bottom of the excavation. Consequently, this leads to the force imbalance and uplift of the pre-existing tunnel [6; 7]. Numerous studies have been conducted on the deformations of existing tunnels caused by excavations. These studies have primarily employed traditional methods such as theoretical analyses, experimental testing, and numerical simulations [8]. Theoretical analyses based on mechanical principles offer a simpler approach and reveal the underlying laws governing engineering problems [9; 10; 11; 12; 13; 14; 15]. However, these methods often require specific assumptions and consider limited parameters, making them challenging to adapt to different project conditions. Experimental testing, on the other hand, provides intuitive results and can better capture the influences of individual factors [16; 17; 18; 19; 20; 21; 22]. However, it may not fully represent actual project conditions, and the associated costs are generally high. Numerical simulations offer quick and effective means of predicting the impacts of excavations on existing tunnel uplifts. They have been widely applied in the engineering field [23; 24; 25; 26; 27]. Nevertheless, the complexities of soils pose challenges in selecting appropriate constitutive models, and the computational capabilities of the software used may impose limitations. Machine learning models are the outcome of research efforts rooted in statistical theory and optimization algorithms with powerful high-dimensional nonlinear fitting capabilities. The fundamental principle underlying machine learning involves constructing models that learn from extensive datasets to identify patterns and regularities. These trained models can then make predictions for new cases while considering all available information throughout the learning process [28; 29]. In the field of underground engineering, various machine learning methods have been applied for prediction purposes, including artificial neural networks (ANNs), random forests (RFs), support vector machines (SVMs), and the extreme gradient boosting (XGBoost) method. Firstly, [PERSON] et al. [30] utilized an ANN method to predict the maximum ground settlement induced by tunnel excavation. Similarly, ANN-based prediction models have been established to intelligently predict soil deformations and geotechnical environmental issues in deep and large excavations and shield tunnel construction projects [31; 32; 33; 34]. [PERSON] et al. [35] employed an RF algorithm to predict the risk associated with deep excavations in subway stations. Additionally, an auto machine learning (AutoML) -based approach has been proposed for the accurate prediction of tunnel displacements induced by excavations, with successful applications in real-world projects [36]. Researchers have also optimized SVM models to assess horizontal displacements and potential risks during the construction of deep excavations, effectively addressing excavation stability assessment challenges [37; 38; 39]. Moreover, a multi-step prediction model for rock displacement around tunnels has been proposed based on an SVM model, and its performance has been compared with that of ANN models [40]. Although the aforementioned machine learning methods are effective, they typically consist of single and complex algorithms. Ensemble methods have emerged as superior approaches when compared to individual machine learning methods and other statistical techniques [41; 42]. Ensemble methods leverage combining multiple models to achieve higher predictive performances and lower error rates. In the field of shield tunnel construction, [PERSON] et al. [43] and [PERSON] et al. [44] developed XGBoost prediction models using different optimization algorithms.