## 1 Introduction  
Pile foundations are widely used to support urban structures, and tunnel-induced ground deformation is transmitted to these piles, which in turn transmit the deformation to the superstructure. The variation in pile settlement and axial force has been extensively studied by many researchers as a means of estimating the state of a pile foundation through field monitoring, and the behavioral characteristics of piles affected by tunnel excavation are also being actively investigated [1; 2; 3; 4; 5; 6; 7; 8; 9]. Since its introduction by [PERSON] [10], piled raft foundations have been applied to numerous large structures. Although many variables must be considered in piled raft design, traditional regression analysis techniques are currently limited to the consideration of only a few variables. In a study on load transfer induced by shield tunnelling in soft ground, [PERSON] et al. [11] investigated changes in the load acting on piles through both field measurements and numerical analysis of the tunnel location. The results of the study indicate that changes in the load due to tunnel excavation are dependent on the pile's location. Specifically, the load on the pile located in the middle of the foundation decreased when the tunnel was excavated under the pile, while the load on the piles at both ends tended to increase to some extent. The authors of this paper intend to compare the outcomes of this study with the numerical analyses conducted in their research. Machine learning, with its ability to consider multiple variables simultaneously, is an effective tool for overcoming the limitations of traditional regression analysis techniquesand is used in many fields, including engineering, natural sciences, and medicine, as well as geotechnical engineering. [PERSON] et al. [12] analyzed single masonry piers with different aspect ratios (h/l) by employing the multi-pier (MP) method. The results of the analysis were used as input to a machine-learning algorithm called back-propagation multilayer perceptron (BPMLP) to generalize the data, and they predicted the potential failure modes of the masonry wall. [PERSON] et al. [13] presented an intelligent model of the hydraulic conductivity of sandy soil by employing various machine-learning algorithms, among which they proposed a GEP-based model to predict the conductivity value with the best performance. They resolved the limitations of the existing value prediction models by using a broad range of input parameters that define geological characteristics, such as grain size, gradation parameters, and dry density. [PERSON] et al. [14] provided a guide to the application of machine learning to various geotechnical problems by reviewing intelligent learning algorithms such as ANN, Fuzzy Logic, GEP, ANFIS, and ANOVA applied to the prediction of geotechnical problems. Therefore, the performance of the final predictive model is affected by the variables of the input data used, missing values, and hyperparameters of the algorithm, which must be considered when utilizing machine learning. According to [PERSON] et al. [15], various geotechnical parameters demonstrate the reliability of developed GSMs through high KPIs (RMSE, MAE, NSE, and PC), and models based on various geotechnical parameters provide efficient design solutions. In particular, according to [PERSON] [16], the quality of the data is a crucial factor in determining the performance of a predictive model. [PERSON] et al. [17] proposed several predictive models to estimate the physico-mechanical characteristics of undisturbed soils. Among them, the 3D response surface-analysis-based models showed 8.2-42.4% less deviation for all parameters compared with the model developed using only DCPI as an independent variable. In the present study, the authors utilized numerical analysis data to develop a model capable of predicting the axial stress induced in piles due to tunnel excavation in the vicinity of a piled raft, through the application of machine-learning techniques.