## 7 Conclusions  
1. The objective function method is used to establish the multi-objective optimization mathematical model of foundation pit dewatering. Combined with the non-dominated sorting genetic algorithm NSGA-II and MATLAB optimization toolbox, the Gamultiobj program is called to develop an iterative program to optimize the pumping capacity of a single well and the number of pumping wells, and the solving process is given. The advantages of multi-objective optimization based on NSGA-II are that the uniformly distributed Pareto optimal solution set can be obtained, and multi-objective optimization problems for foundation pit dewatering can more quickly and efficiently be handled based on the fast-elite selection strategy. Using the Analytic Hierarchy Process (AHP) combined with the evaluation scoring method to establish an evaluation system, the candidate set with high scores in the Pareto solution set is used as the decision-making basis. The construction of an evaluation system combining NSGA-II and AHP applied in foundation pit dewatering engineering represents an innovative technology and method. 2. Using the NSGA-II algorithm and MATLAB optimization toolbox programming, the dewatering optimization of the foundation pit project of the inverted siphon section of the canal head (pile No. XZ0+326-XZ0+500) in the water conservancy and irrigation area engineering of the Xixiayuan water conservancy project was carried out, and the Pareto optimal solution set under three optimal conditions (24 to 26 pumping wells in running) was obtained. By incorporating the dewatering multi-objective optimization evaluation system based on the Analytic Hierarchy process, the optimization scheme within the set of Pareto optimal solutions is chosen as the ultimate decision for optimization. This scheme takes into account three objectives: the total dewatering cost, the settlement influence coefficient, and the safety and stability of the foundation pit structure. Consequently, it offers a range of workable plans for the construction of the foundation pit dewatering. 3. The study area's numerical model is created using the MODFLOW module and SUB subroutine package within GMS. The optimization outcomes for the decision variables \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Types**} & **The Distance Where Water** & **Mean Value of Ground** & **Total Pumping Wells** \\  & **Level in the Center of the** & **Settlement Observation** & **Flow/(m3/d)** \\  & **than the Bottom Plate/(m)** & **Points/(mm)** & **Flow/(m3/d)** \\ \hline Allowable value & \(>\)0 & \(<\)15 & \(<\)144,000 \\ Optimal results & 1.84 & 7.95 & 143,665 \\ Numerical simulation results & 1.40 & 8.9 & 143,815 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Verification and comparison of different results under the third optimal working condition. in the dewatering scheme, which involves operating 24 pumping wells and closing the J19 and J22 pumping wells based on the third optimal condition, are applied to the numerical model. The numerical simulation of this optimized scheme validates the scientific nature and accuracy of the multi-objective optimization model for foundation pit dewatering. Importantly, the established multi-objective optimization model and evaluation system offer numerous viable dewatering optimization plans. Conceptualization, Z.M. and Y.Z. ; methodology, Z.M. and Y.Z. ; validation, Y.W. and B.L. ; formal analysis, Z.M., Y.Z. and Y.W. ; resources, Z.M. and Y.Z. ; data curation, Y.W. and B.L. ; writing--original draft preparation, Z.M. and Y.Z. ; writing--review and editing, J.W., B.L. and Y.W. ; visualization, B.L. and Y.W. ; supervision, Y.Z. and J.W. ; project administration, Z.M., Y.Z. and J.W. ; funding acquisition, Y.Z. and J.W. All authors have read and agreed to the published version of the manuscript.