Space grid structures are popularly used in large-span civil structures. Identification of the axial internal forces and boundary conditions of the space grid structural members is very important. However, in the engineering practice, the boundary conditions of such members are not ideally rigid or pinned. They are sometimes semi-rigid conditions. Besides, direct measurements of the axial forces and the boundary rigidities of in-situ space grid structural members are difficult to make. Therefore, in this study, a Particle Swarm Optimization (PSO) algorithm-based axial force and boundary rigidity identification method is proposed for space grid structural members using multi-order natural frequencies, which can be easily obtained from in-situ tests. The theoretical background of the proposed method is discussed in detail, including the coupling relationship between the axial force and natural frequency and the PSO algorithm. The applicability and accuracy of the proposed method are validated through experimental tests and comparative analyses.

空间网格结构广泛用于大跨度的土木结构中。识别空间网格结构构件的轴向内力和边界条件非常重要。但是，在工程实践中，这种构件的边界条件不是理想的刚性或固定的。它们有时是半刚性条件。此外，难以直接测量现场空间网格结构构件的轴向力和边界刚度。因此，在这项研究中，提出了一种基于粒子群优化算法的轴向力和边界刚度识别方法，该方法利用多阶固有频率对空间网格结构构件进行了识别，该方法可以很容易地从现场测试中获得。详细讨论了该方法的理论背景，包括轴向力和固有频率之间的耦合关系以及PSO算法。通过实验测试和比较分析验证了该方法的适用性和准确性。