## 1 Introduction  
Tunneling inevitably initiates ground deformations. This affects adjacent structures' behaviors, with variables such as the relative density of the ground, groundwater level, structure type, and tunnel location. Tunneling in urban areas is essential to understand the interactive behavior of pile foundation and adjacent structures, and many studies have been carried out on this through closed form, numerical analysis [1; 2; 3; 4], 1 g laboratory model testing [5], centrifuge model testing [6; 7], and field monitoring [8; 9; 10]. Among foundation types of structure, the piled raft is more rational than the conventional pile foundation, which does not consider the supporting load of the raft. In a piled raft, the pile is considered to act as a settlement reducer of the raft, and settlement in a piled-raft system is a very important factor in understanding the system.  
When tunneling is not considered, since both the raft and the bottom of the pile show compression behavior, the difference between the settlement of the raft and the settlement of the pile may not be significant. If tunneling is considered, the amount of ground settlement varies depending on the distance from the tunnel, and it is also unclear whether ground deformation by tunneling acts in the same way as the raft does.  
However, existing studies related to piled rafts were conducted without dividing the settlement into raft and pile; furthermore, only few variables were considered for the piled raft-tunneling problem. When the difference of settlement between the raft and pile due to tunneling is huge, the skin of the pile cracks, and has direct effect on the stability of the pile and structures. Thus, the aim of this study is important to understand the relationship between the difference of settlement between raft and pile tip and its impact on pile concrete.  
Machine learning could be utilized to solve the problems mentioned earlier. Particularly as the geotechnical engineering field faces huge uncertainty concerning materials such as sand, clay, rock, etc., many researchers and engineers have tried to reduce the amount of difference or estrangement between numerical analysis, laboratory model tests and real phenomena on the construction site. For this matter, machine learning will be a very powerful and useful tool that could be utilized not only for uncertainty in geotechnical engineering, but also in medical [11; 12], mechanical [13; 14] and engineering fields, etc.  
In this study, therefore, when tunneling adjacent to the piled raft was performed, a laboratory model test and numerical analysis were conducted to analyze settlement by separating raft and pile. In the laboratory model test, the settlement of the raft in the piled raft was measured, and in numerical analysis the settlement of raft and pile was measured. In addition, machine learning was utilized using settlement data derived from numerical analysis for the settlement prediction model. Through this model, the laboratory model test result was predicted and compared to verify the accuracy of the prediction model; on this basis, pile settlement for a piled-raft prediction model was derived. A model for predicting the rate of change of settlement according to the pile length was also developed.