夏志远, 李爱群, 李建慧, 陈鑫. 基于GMPSO的有限元模型修正方法验证[J]. 工程力学, 2019, 36(10): 66-74. DOI: 10.6052/j.issn.1000-4750.2018.09.0506
引用本文: 夏志远, 李爱群, 李建慧, 陈鑫. 基于GMPSO的有限元模型修正方法验证[J]. 工程力学, 2019, 36(10): 66-74. DOI: 10.6052/j.issn.1000-4750.2018.09.0506
XIA Zhi-yuan, LI Ai-qun, LI Jian-hui, CHEN Xin. VALIDATION OF FINITE ELEMENT MODEL UPDATING METHODOLOGY BASED ON GMPSO[J]. Engineering Mechanics, 2019, 36(10): 66-74. DOI: 10.6052/j.issn.1000-4750.2018.09.0506
Citation: XIA Zhi-yuan, LI Ai-qun, LI Jian-hui, CHEN Xin. VALIDATION OF FINITE ELEMENT MODEL UPDATING METHODOLOGY BASED ON GMPSO[J]. Engineering Mechanics, 2019, 36(10): 66-74. DOI: 10.6052/j.issn.1000-4750.2018.09.0506

基于GMPSO的有限元模型修正方法验证

VALIDATION OF FINITE ELEMENT MODEL UPDATING METHODOLOGY BASED ON GMPSO

  • 摘要: 为提高有限元模型修正方法效率,保证修正精度,提出基于高斯白噪声扰动的粒子群优化(GMPSO)有限元模型修正方法。介绍标准粒子群优化(PSO)方法和改进后的GMPSO方法,基于测试函数比对两种方法的全局寻优能力和寻优效率;提出高效的基于GMPSO有限元模型修正方法,阐述方法流程并明确各参数与实际物理量的对应关系;基于GMPSO有限元模型修正方法对高维有损伤简支梁模型(变量维度为10)实施修正,并与基于遗传算法(GA)的模型修正结果进行比对;基于GMPSO有限元模型修正方法对某在役桥梁结构实施修正(变量维度为13),验证所提方法可行性。结果表明:经局部改进的GMPSO方法较原PSO方法的优化能力显著提升;高维损伤简支梁模型修正结果显示,基于GMPSO模型修正方法可获得较好的修正结果,修正效率较基于GA的模型修正方法有显著提升;在役桥梁结构有限元模型修正结果显示,基于GMPSO模型修正方法可有效降低主梁计算频率和试验频率的误差,所提方法可适用于较工程复杂结构模型修正问题。

     

    Abstract: A FE Model updating approach based on Gaussian white noise mutation particle swarm optimization (GMPSO) is proposed to improve the efficiency of the model updating method and keep its accuracy. The particle swarm optimization (PSO) and the improved method GMPSO were introduced and the global optimization searching capacity was compared between the two methods. The FE model updating method based on GMPSO was then proposed and the one-to-one match between the parameters in the method and the real physical variables was discussed together with the method process. The new approach was applied to the model updating of a damaged simply supported beam (DSSB) with high-dimension (with ten variables) and the updating results was compared with that of model updating method based on Genetic Algorithm (GA). Meanwhile, the method is also applied to an in-serviced bridge structure (with thirteen variables) to validate the feasibility of the proposed method. The results show that:the global optimization searching capacity of the improved GMPSO is significantly higher than that of PSO; the relationships between the parameters and physical variables are clear in the model updating method based on GMPSO and this is suitable for modularized program operation and facilitate the application of commercial software. The updating process of the DSSB with high-dimension using the proposed methodology reveals accurate results and its efficiency is significantly improved, compared with the process of model updating based on GA. The updating of the in-serviced bridge using the proposed methodology narrows the differences between the main girder vibration frequencies of model and those of test. The feasibility of the application of the proposed model updating methodology to the updating issue of high-dimensional and complex engineering structures is verified.

     

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