School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China.
Department of Civil and Environmental Engineering, National University of Singapore, 2 Engineering Drive 2, Singapore 117576, Singapore.
Sensors (Basel). 2018 Jun 8;18(6):1879. doi: 10.3390/s18061879.
This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.
本研究应用克里金模型和粒子群优化（PSO）算法，通过大振幅初始条件下的高阶振动模态，实现桥梁结构的动态模型更新。在利用时域运行模态分析解决高阶模态识别理论后，基于拉丁超立方抽样和回归分析，建立了克里金模型。克里金模型作为复杂有限元模型的替代模型，用于预测分析响应。建立了一个目标函数来表示分析预测响应与实验测量响应之间的相对差异，然后使用 PSO 算法对初始有限元（FE）模型进行更新。采用 Jalán 高架桥（混凝土连续铁路桥）验证了所提出的方法。结果表明，克里金模型可以准确预测响应，同时减少计算时间。
