Model Updating for a Continuous Concrete Girder Bridge Using Data from Construction Monitoring

被引:5
|
作者
Cheng, Xiao-Xiang [1 ]
机构
[1] Southeast Univ, Sch Civil Engn, Nanjing 211189, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
finite element model updating; continuous concrete girder bridge; construction monitoring; cantilever casting; stress measurement; uneven temperature; grey theory;
D O I
10.3390/app13063422
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Finite element (FE) model updating can guarantee the accuracy of the numerical analyses for civil structures. However, two deficiencies currently exist with the traditional FE model updating technique based on the measurements of modal parameters and/or the static structural responses of the built structure with respect to its reference information insufficiency and its non-unique solution generally obtained, hampering its extensive use. It becomes the goal of the whole engineering community to introduce new effective methods for the civil structural FE model update. To this end, an innovative FE model updating method using data from construction monitoring is proposed in this article. With regard to its theoretical novelty, the new method transforms the complicated multi-variable optimization mathematical problem with the traditional FE model updating technique into many simple single-variable parameter identification problems. Under the engineering background of Huangsha Harbor Bridge, a three-span concrete continuous box girder bridge constructed utilizing the symmetric cantilever casting method, the effectiveness and the efficiency of the new model updating practice were validated. It is demonstrated using quantitative data that the abundant data measured on Huangsha Harbor Bridge in construction stages can enhance the reference information for the more accurate FE model updating of the structure, and the uncertain parameters with the initial FE model of Huangsha Harbor Bridge can be progressively and easily identified for the proposed model updating method using many single-variable linear regression models, instead of one complicated multi-variable mathematical or numerical model employed by the traditional model updating approaches, which generally leads to non-unique solutions rendered by normal optimization algorithms.
引用
收藏
页数:20
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