## 6 Conclusions  
In this study, the settlement behavior of piled rafts due to tunneling was analyzed, and prediction models were developed using machine learning. The prediction model was studied by dividing it into raft settlement and pile settlement. On this basis, the settlement change rate along the pile length was analyzed.  
The raft settlement obtained through the laboratory model test was compared with the measured value using the developed prediction model for raft settlement. In addition, the influence of features on the prediction model of raft settlement, pile settlement, and change rate of settlement along the pile length was quantitatively analyzed through XGB.  
* Laboratory model tests were performed on loose ground, and raft settlement was analyzed as the horizontal offset between pile toe and tunnel crowns increased, where the vertical offset between pile toe and tunnel crowns was maintained at 0.5 D. \(P_{N}\) being 1, raft settlement of 0.19, 0.16, and 0.07 mm occurred at 0.0, 1.0, and 2.0 D, respectively. When \(P_{N}\) was 2, it was 0.31, 0.12, and 0.10 mm; at \(P_{N}\) = 3, it was 0.36, 0.20, and 0.11 mm. When horizontal offset between pile toe and tunnel crown was maintained at 0.0 D and vertical offset between pile toe and tunnel crown was 0.5, 1.0, 2.0 D, for \(P_{N}\) = 1, raft settlement of 0.19, 0.15, and 0.14 mm occurred; at \(P_{N}\) 2, this was 0.31, 0.19, and 0.18 mm; and when \(P_{N}\) was 3, this was 0.36, 0.30, and 0.27 mm, respectively.
* The prediction model of raft settlement showed better performance in XGB with 2.16 for XGB and 2.44 for MLP. As a result of applying this to the laboratory model test data, it showed a value of 0.02 for XGB, and 8.23 for MLP. This is considered to be an error caused by a significantly smaller number of laboratory model tests data counts compared to FE analysis data. This indicates that MLP is more reliant on the number of data compared to XGB.  
Figure 15: Feature importance of prediction models.  
In the prediction model of raft settlement derived through XGB, feature \(P_{L}\) had an effect of 0.234, T\({}_{\rm F}\) was 0.174, O\({}_{\rm V}\) was 0.165, and O\({}_{\rm H}\) was 0.118, showing an effect of more than 10%. On the other hand, E\({}_{\rm G}\), P\({}_{\rm CTC}\), B\({}_{\rm P}\), and P\({}_{\rm N}\) were 0.090, 0.074, 0.074, and 0.071, respectively, showing less than 10% effect.
* XGB and MLP had an RMSE of 1.63 and 2.32 in the prediction model of pile settlement, respectively. \(P_{L}\) had the highest importance of 0.22, and T\({}_{\rm F}\), O\({}_{\rm V}\) and O\({}_{\rm N}\) were 0.152, 0.148, and 0.106, respectively. These features affected the prediction model of pile settlement by more than 10%. E\({}_{\rm G}\), O\({}_{\rm H}\), B\({}_{\rm P}\) and P\({}_{\rm CTC}\), which were 0.099, 0.097, 0.091, and 0.085, respectively, affected this model by less than 10%.
* The prediction model of settlement change rate along the pile length is a concept in which the difference between raft and pile settlement due to tunneling is normalized by pile length, and it was assumed to be linear in this study. In the prediction model of settlement change rate along the pile length, the RMSE from the XGB algorithm was 0.05, and MLP was 0.07, respectively. In the obtained values from the prediction model, the maximum was 0.64 and the minimum was \(-\)0.71.  
In the prediction model of change rate of settlement along the pile length, O\({}_{\rm H}\) showed importance of 0.183, and T\({}_{\rm F}\), O\({}_{\rm V}\), \(P_{L}\), and E\({}_{\rm G}\) showed importance of 0.171, 0.165, 0.155, and 0.108, respectively. In the case of P\({}_{\rm CTC}\), B\({}_{\rm P}\), and P\({}_{\rm N}\), importance of 0.094, 0.066, and 0.065 was less than 10%.