The optimization of structural parameters, such as mass(m), stiffness(k), and damping coefficient(c), is critical for designing efficient, resilient, and stable structures. Conventional numerical approaches, including Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) simulations, provide high-fidelity results but are computationally expensive for iterative optimization tasks, as each evaluation requires solving the governing equations for every parameter combination. This study proposes a hybrid data-driven framework that integrates a Graph Neural Network (GNN) surrogate model with a Genetic Algorithm (GA) optimizer to overcome these challenges. The GNN is trained to accurately learn the nonlinear mapping between structural parameters and dynamic displacement responses, enabling rapid predictions without repeatedly solving the system equations. A dataset of single-degree-of-freedom (SDOF) system responses is generated using the Newmark Beta method across diverse mass, stiffness, and damping configurations. The GA then searches for globally optimal parameter sets by minimizing predicted displacements and enhancing dynamic stability. Results demonstrate that the GNN and GA framework achieves strong convergence, robust generalization, and significantly reduced computational cost compared to conventional simulations. This approach highlights the effectiveness of combining machine learning surrogates with evolutionary optimization for automated and intelligent structural design.

翻译：结构参数（如质量(m)、刚度(k)和阻尼系数(c)）的优化对于设计高效、韧性和稳定的结构至关重要。传统的数值方法，包括有限元法(FEM)和计算流体动力学(CFD)模拟，能够提供高保真度的结果，但对于迭代优化任务计算成本高昂，因为每次评估都需要为每个参数组合求解控制方程。本研究提出了一种混合数据驱动框架，将图神经网络(GNN)代理模型与遗传算法(GA)优化器相结合，以克服这些挑战。GNN被训练以准确学习结构参数与动态位移响应之间的非线性映射，从而无需重复求解系统方程即可实现快速预测。使用Newmark Beta方法在多种质量、刚度和阻尼配置下生成了单自由度(SDOF)系统响应的数据集。随后，GA通过最小化预测位移并增强动态稳定性来搜索全局最优参数集。结果表明，与传统模拟相比，GNN与GA框架实现了强收敛性、鲁棒泛化能力以及显著降低的计算成本。该方法凸显了将机器学习代理模型与进化优化相结合，在自动化和智能化结构设计中的有效性。