## 1 Introduction
They designed an optimization scheme with the gate weight as the objective function and structural strength and stability as constraints, achieving a reduction in the weight of planar steel gates and lowering the cost. [PERSON] [14] took the interference connection structure of a turbine rotor as the research object, designed a profile structure, and established a stress prediction model of the interference connection structure. Based on the stress prediction model, the influence of different profiles on the state of stress distribution was analyzed. Through the optimization design, the structural reliability was ultimately improved. In order to conduct an optimization design of electromagnetic drive structure for a shaftless rim thruster, [PERSON] [15] selected four parameters, including efficiency and starting current, as optimization objectives, and six parameters related to slot type as optimization variables, and used BP neural network to find the mapping relationship between optimization variables and optimization objectives. Under the combination of BP prediction model and optimization function, the efficiency of the motor was improved by 3.01%. [PERSON] [16] established a regression equation for the prediction model of a CSG dam response surface using the finite element method. Using the regression equation of the prediction model as the objective function, the artificial bee colony algorithm was used to optimize the profile form and gel content of the CSGdam, which reduced the horizontal displacement of the dam by 36.9% and the vertical displacement by 25.5%. Currently, there is a scarcity of studies on the optimization of dam section design using techniques that combine neural network prediction models like BP neural networks with design optimization. [PERSON] applied artificial intelligence methods to the design of CSG dams, but did not consider the economic benefits of the dam body. This paper combines a BP neural network and the Pattern Search tool based on MATLAB R2020a, making it easy to achieve complex optimization requirements and considering economic benefits in optimization. Due to the strong ability of self-learning and nonlinear mapping, a BP neural network can be used for predicting the stress and deformation of CSG dams, which are nonlinear materials. In the section design optimization of CSG dams, stress and deformation are often obtained as constraints of functions through finite element calculations. In this paper, stress and deformation in the constraint are provided by a trained BP neural network, thereby improving computational efficiency. This paper relies on a CSG dam with two stages in the design phase. Considering the geometric parameters of the dam body, including the upstream and downstream slope coefficients of the first and second stages of the dam body, and the material properties of the overburden, it analyzes the influencing factors of the maximum settlement and maximum compressive stress of the dam body during the impounding period. A grey relational analysis for each influencing factor in relation to maximum settlement and maximum compressive stress is introduced. A BP neural network model is established, whose accuracy of data regression will be tested. Relying on the trained model, the mapping relationship between the geometric parameters and the stress and displacement of the dam body is found, and the structural design optimization of the dam body is realized. The roadmap of the study's flow of this paper is shown in Figure 1.