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基于Kriging代理模型的迭代更新高效反演方法
Efficient iterative updating inversion method based on Kriging surrogate model
投稿时间:2022-03-14  修订日期:2022-04-20
DOI:
中文关键词:  参数反演  Kriging代理模型  粒子群优化  迭代更新
英文关键词:parameter inversion  Kriging surrogate model  particle swarm optimization  iterative updating
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位邮编
周广得 河海大学工程力学系 211100
吕小龙 河海大学工程力学系 
黄丹 河海大学工程力学系 211100
姜冬菊 河海大学工程力学系 
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中文摘要:
      为提高混凝土坝等大体积结构参数反演效率和精度,减少由于应用有限元进行大量正分析而产生的计算机时,建立了一种结合Kriging代理模型和粒子群优化(PSO)算法的迭代更新反演方法。通过拉丁超立方抽样(LHS)方法确定初始样本点的空间分布,并使用有限元正分析获取对应的响应值,构建粗糙的初始代理模型,结合具有全局寻优能力的PSO算法,反演大体积结构的分区弹性模量,随之再代入有限元模型中,计算获取新的位移响应,并将其作为新样本加入到样本集中,通过迭代更新获得局部更高精度的代理模型。工程实际算例表明,该方法对混凝土坝等大体积结构参数反演精度较高、适用性好,且能大幅减少传统有模型反演方法所需消耗的正分析机时,提高反演效率。
英文摘要:
      To improve the efficiency and accuracy of inverse analysis of massive structures like concrete dam, as well as to reduce the computational cost for finite element analysis, a new efficient iterative updating inversion method was constructed based on the recently developed Kriging surrogate model and particle swarm optimization (PSO) algorithm. The spatial distribution of the initial sample points was determined by the Latin hypercube sampling (LHS) method, and obtaining the corresponding responses by the finite element method to obtain the samples with which a roughly initial Kriging model can be constructed. Inversing the regional elastic modulus of the massive structures by using a combination of the surrogate model and PSO algorithm with excellent global optimization performance. The parameters obtained from inversion are applied to finite element analysis to get the corresponding displacements and combining them as a new sample adding to the sample set to update the surrogate model for higher accuracy. Numerical examples show that the proposed hybrid method is suitable for inverse analysis of massive structures such as concrete dam with higher accuracy and efficiency by remarkably reducing the computational cost for normal analysis in traditional model-induced inversion.
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