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Model updating of an existing bridge with high-dimensional variables using modified particle swarm optimization and ambient excitation data
Measurement ( IF 5.6 ) Pub Date : 2020-03-19 , DOI: 10.1016/j.measurement.2020.107754
Zhiyuan Xia , Aiqun Li , Jianhui Li , Huiyuan Shi , Maojun Duan , Guangpan Zhou

Model updating for bridge engineering structures to obtain precise finite element model is an efficient tool at present. But with the dimension of the potential parameters during optimization increasing, it brings a challenge to present optimization methods. To improve the performance of convergence process and the probability of detecting global extremum, a combination model updating method of modified particle swarm optimization (MPSO) and response surface method was proposed herein. MPSO has less modification to PSO as only modifying the particle position with Gaussian white noise at a probability and the performance was significantly superior than PSO illustrated by testing functions. A beam model updating example based on the proposed method was tested, the convergence ability and accuracy were compared with genetic algorithm which is widely applied in bridge model updating. Finally, the proposed method was successfully applied to model updating of an existing bridge engineering structures with thirteen variables.



中文翻译:

使用改进的粒子群算法和环境激励数据对现有具有高维变量的桥梁进行模型更新

目前,桥梁工程结构的模型更新以获得精确的有限元模型是一种有效的工具。但是,随着优化过程中潜在参数的增加,给当前的优化方法带来了挑战。为了提高收敛过程的性能和检测全局极值的可能性,提出了一种改进的粒子群优化算法和响应面法相结合的模型更新方法。MPSO对PSO的修改较少,因为仅以概率修改带有高斯白噪声的粒子位置,并且性能明显优于测试功能所示的PSO。测试了基于该方法的梁模型更新示例,将其收敛能力和准确性与广泛应用于桥梁模型更新的遗传算法进行了比较。最后,该方法成功地应用于具有十三个变量的现有桥梁工程结构的模型更新。

更新日期:2020-03-19
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