## 1 Introduction  
With the ongoing development of urbanization in China, the high-rise and super-high-rise buildings and the infrastructure such as roads, subways, and utility tunnels spring up continuously in major cities, which make the effective space of urban construction smaller and the construction environment more complex. The existing infrastructure and buildings are inevitably affected by adjacent excavation. Therefore, the safety of the surrounding environment during excavation has become a focus of attention, and much fruitful research on the hot issues of underground excavation and its induced site deformation in infrastructure construction has been published [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Meanwhile, the safety of existing buildings and utilities close to excavation is receiving more and more attention [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. The major methods [12; 29] for investigating the response of the surrounding environment induced by adjacent excavation include analytic approaches [14], empirical formulae [30], numerical simulations [1; 9], field monitoring [7], and physical model tests [24; 28; 31]. Among them, the analytic approach is based on the theory of elasticity, which has too many assumptions and cannot represent the nonlinear properties of the site. The empirical formulas are mostly proposed for specific objects, and the parameters in the formula need to be determined by fitting, thus they can only be used to predict the final response. The numerical method can simulate complex conditions, but the results are affected by the selection of material constitutive relationships and the treatment effect of boundary conditions, so it is difficult to consider some unexpected conditions in construction. The physical model tests include the 1 g physical model test and the centrifuge model test, the geometric scale model is used for the test, and other the physical model parameters need to be determined according to the similarity law. However, the similarity of physical model test is only suitable for the elastic stage, and too many assumptions make it impossible to be completely faithful to the engineering prototype. The field-monitoring method is to monitor the response of objects in the whole excavation process, which is undoubtedly the most reliable means. However, due to the limitation of site conditions, some monitoring may be interrupted or unable to be carried out during excavation. For example, the subway station, tunnel, utility tunnel, buried pipeline, and other concealed works cannot be directly monitored for a variety of reasons, and the monitoring of surrounding buildings may be interrupted due to the change in on-site construction conditions. Machine learning provides a new ideas, and has been applied in the research of excavation engineering. For example, [PERSON] and [PERSON] (2021) [32], [PERSON] et al. (2021) [33], [PERSON] et al. (2022) [34], [PERSON] et al. (2022) [35], [PERSON] et al. (2022) [36], [PERSON] et al. (2023) [37], [PERSON] et al. (2023) [38], and [PERSON] et al. (2022) [39] directly used or improved different machine-learning methods, such as expanding deep-learning methods, TS-BPNN, random forest, STF-Networks, and hybrid algorithms, etc., to predict the ground settlement induced by excavation. [PERSON] et al. (2021) [40] established a dynamic prediction method for excavation-induced concrete diaphragm wall (CDW) deformation based on machine learning, and compared the three algorithms of back-propagation neural network (BPNN), long short-term memory (LSTM), and gate recurrent unit (GRU). [PERSON] et al. (2020) [41] presented a simple process-based model consisting of an artificial neural network module, inverse modelling module, and mechanistic module for predicting excavation-induced tunnel responses. [PERSON] et al. (2022) [42] proposed an improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. [PERSON] et al.