Adaptive Kriging model based finite element model updating of a cable-stayed pedestrian bridge

QIN Shiqiang; LIAO Sipeng; HUANG Chunlei; TANG Jian

doi:10.13471/j.cnki.acta.snus.2020.02.12.2020B009

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Adaptive Kriging model based finite element model updating of a cable-stayed pedestrian bridge

Research articles | 更新时间：2023-11-01

- Adaptive Kriging model based finite element model updating of a cable-stayed pedestrian bridge
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 60, Issue 6, Pages: 43-53(2021)
- 作者机构：
  
  武汉理工大学土木工程与建筑学院，湖北武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2020.02.12.2020B009
  CLC： U448.22
- Published：25 November 2021，
  
  Published Online：09 January 2021，
  
  Received：12 February 2020，
  
  Accepted：04 March 2020
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秦世强,廖思鹏,黄春雷等.基于自适应Kriging模型的人行斜拉桥有限元模型修正[J].中山大学学报(自然科学版),2021,60(06):43-53.

QIN Shiqiang,LIAO Sipeng,HUANG Chunlei,et al.Adaptive Kriging model based finite element model updating of a cable-stayed pedestrian bridge[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(06):43-53.
秦世强,廖思鹏,黄春雷等.基于自适应Kriging模型的人行斜拉桥有限元模型修正[J].中山大学学报(自然科学版),2021,60(06):43-53. DOI： 10.13471/j.cnki.acta.snus.2020.02.12.2020B009.

QIN Shiqiang,LIAO Sipeng,HUANG Chunlei,et al.Adaptive Kriging model based finite element model updating of a cable-stayed pedestrian bridge[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(06):43-53. DOI： 10.13471/j.cnki.acta.snus.2020.02.12.2020B009.

摘要

为了更加合理地确定Kriging模型样本点数量，提高Kriging模型对目标函数极值区域的预测精度，提出了一种自适应Kriging模型并将其应用于桥梁结构有限元模型修正。该方法首先通过中心复合设计估算初始样本点数量，然后利用拉丁超立方设计获得初始样本点的空间分布，并初步构建Kriging模型；最后利用期望改善准则（EI准则）控制新增样本点位置，使其位于目标函数极值附近区域；新增样本点的最终数量由收敛准则确定。利用测试函数比较了自适应Kriging模型和标准Kriging模型的预测精度；并以一座人行斜拉桥模型修正为例，对比了基于自适应Kriging模型和标准Kriging模型的修正结果。结果表明：在样本点总数一致的情况下，自适应Kriging模型与标准Kriging模型精度评价指标基本一致，但自适应Kriging模型能够避免样本点空间分布的随机性，提高对目标函数极值区域的预测精度，从而获得更好的修正结果。

Abstract

To reasonably determine the number of data samples for Kriging model and to improve the predicting accuracy of Kriging model at minimum areas of objective function， this study proposes an adaptive Kriging model and applies it in the finite element model updating of bridge structures. The proposed method first estimates the initial number of data samples using central composite design， then Latin hypercube design is utilized to obtain the spatial distribution of data samples. The Kriging model is constructed based on the initial data samples set. Finally， the expected improvement （EI） criterion is employed to control the location of newly increased data samples， making them mainly locate at the minimum areas of objective function. The number of newly increased data samples are determined by convergence criterion. The predicting accuracy of standard Kriging model and adaptive Kriging model are compared by using test functions. The model updating of a pedestrian cable-stayed bridge is taken as an example， in which the updating results of Kriging model and adaptive Kriging model are compared. The results show that the accuracy indexes of Kriging model and adaptive Kriging model are almost the same under that the premise the total number of data samples is equal. However， the adaptive Kriging model can avoid the random distribution of data samples in design space， thus provide a higher predicting accuracy in minimum areas of the objective function and obtain better updating results.

关键词

桥梁工程自适应Kriging模型模型修正期望改善准则代理模型

Keywords

bridge engineeringadaptive Kriging modelmodel updatingexpected improve criterionsurrogate model

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