Stiffness Distribution Updating Method for Box Girder Bridges Considering Nonuniform Cross-Section Stiffness
Hong Zhou, Ting-Hua Yi, Dong-Hui Yang, Jian Zhang, Wen-Jie Li, Ying-Sheng NiAbstract
Under long-term coupled effects of various loads, the stiffness distribution within box girder bridge cross sections becomes nonuniform. Moreover, as thin-walled closed sections, box girders exhibit more complex stress–strain states compared to conventional members. Consequently, research focusing on stiffness degradation and its implications for the complex stress state of box girder bridges is essential. This paper proposes a method for updating the stiffness distribution of box girder cross sections that considers nonuniform cross-section stiffness. The Generalized Beam Theory (GBT) is employed to accurately analyze the stress–strain state of the box girder cross section. The recommended discretization approach and analysis procedure for the box girder cross section are presented. Based on the GBT analysis, a strain gauge layout scheme is designed to obtain more robust responses that effectively reflect local stiffness variations. The updating process for cross-section stiffness distribution consists of two steps: first, traditional model updating is performed to obtain the global cross-section stiffness; second, the internal stiffness distribution of the cross section is determined by updating multiple stiffness coefficients within it. The proposed method is validated through numerical examples and field tests on an actual bridge. Results demonstrate that the updated model achieves good agreement with measured strains, with the average error significantly reduced. The proposed updating strategy enables independent analysis of stiffness distribution variations within local beam segments, achieving multimatching between global and local structural responses at different locations. This approach provides a more accurate basis for stress analysis and load-bearing capacity assessment of box girder bridges under service conditions.