## 5 Conclusions  
To overcome the limitations of the Informer algorithm in predicting surface settlement induced by shield tunneling, we proposed an improved algorithm and verified its accuracy. The improved Informer algorithm was then input to the SHAP algorithm to analyze the effects of key tunneling parameters on surface settlement. Finally, a multi-objective \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Combination of** \\ **Excavation Parameters** \\ \end{tabular} & **Thrust (kN)** & \
\begin{tabular}{c} **Cutterhead** \\ **Torque (kN-m)** \\ \end{tabular} &
\begin{tabular}{c} **Advance Rate** \\ **(mm/min)** \\ \end{tabular} & **Settlement (mm)** \\ \hline \multirow{4}{*}{Original} & 11,300 & 2460.9982 & 39.0000 & \(-\)10.247 \\  & 11,500 & 2337.2135 & 40.0000 & 2.171 \\  & 11,500 & 2311.4430 & 42.0000 & \(-\)7.520 \\  & 11,300 & 2201.9703 & 45.0000 & \(-\)4.071 \\  & 11,700 & 2317.9262 & 47.0000 & \(-\)6.315 \\  & 11,714 & 2449.8298 & 38.8373 & 1.2623 \\  & 11,402 & 2447.8458 & 40.0938 & \(-\)1.2648 \\ \multirow{2}{*}{Optimized} & 11,600 & 2352.7812 & 40.3691 & \(-\)2.9640 \\  & 11,453 & 2251.7883 & 44.8257 & \(-\)0.7665 \\  & 11,732 & 2448.3156 & 45.0416 & \(-\)5.1628 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of original and optimized tunneling parameters. optimization algorithm based on the NSGA-III method was developed to optimize the shield tunneling parameters. Our key findings are summarized as follows:  
1. To address the limitations of the Informer algorithm in predicting surface settlement, a dilated causal convolutional network was employed to replace the standard convolutional network, and three improvements were made: soil layer classification and characterization, a moving prediction window, and incorporation of special factors. These measures yielded the improved Informer algorithm. 2. Compared with the original Informer algorithm, the improved Informer algorithm had obvious advantages: 1 The prediction accuracy improved significantly. Through the improvements, the R\({}^{2}\) value increased from 0.79 to 0.86, the MSE decreased from 5.22 to 4.13, the MAE decreased from 5.91 to 3.65, and the RMSE decreased from 2.28 to 2.03. 2 With increasing prediction timescales, the rate of R\({}^{2}\) reduction for the improved Informer algorithm was consistently lower than that of the original algorithm. Both the perception range and accuracy of the improved algorithm were optimized. 3 The improved Informer algorithm had a shorter prediction time than the original algorithm. The prediction time was reduced by up to 30.64%. Compared with the RF and LSTM algorithms, the improved Informer algorithm also exhibited superior performance. It is more suitable than other algorithms for predicting surface settlement in metro shield tunneling projects. 3. According to the SHAP theory, we analyzed the effects of shield tunneling parameters on surface settlement. The results indicated that shield thrust was the most critical feature for predicting surface settlement. Its impact on surface settlement needed to be judged in conjunction with soil layer conditions. The cutterhead torque had a negative correlation with its SHAP values. Low tunneling speeds exerted a negative impact on settlement, while high speeds had a positive impact. Additionally, the tunneling speed and thrust exhibited a strong negative correlation. Grouting pressure and muck pressure positively influenced surface settlement at low levels but negatively influenced it at high levels. During the construction process, appropriate increases in thrust torque, grouting pressure, and muck pressure, combined with reduced tunneling speed, were found to effectively mitigate surface settlement. 4. Shield thrust, cutterhead torque, and tunneling speed were selected as optimization parameters. On this basis, a multi-objective optimization algorithm was developed and solved using the NSGA-III algorithm, with the aim of optimizing the combinations of shield tunneling parameters.