Suspension bridge response assessment under temperature-vehicle combined loading: An analytical algorithm
Suspension bridges’ excellent spanning capacity is compromised by their insufficient stiffness, making their live load response quite challenging. This study introduces an analytical method for solving the response of the suspension bridge under the temperature-vehicle combined loading. The deformat...
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Published in | Advances in structural engineering |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
25.06.2025
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Online Access | Get full text |
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Summary: | Suspension bridges’ excellent spanning capacity is compromised by their insufficient stiffness, making their live load response quite challenging. This study introduces an analytical method for solving the response of the suspension bridge under the temperature-vehicle combined loading. The deformation of the main cable is estimated based on the elastic catenary theory. The variation of internal forces expresses the pylon’s and the beam’s deformation. Displacements of key nodes are estimated and set as constraints. Given the high non-linearity and complexity of suspension bridges, the residual sum of squares of all constraints is the objective function in an optimization algorithm. The analytical solutions to the system of equations are found by employing the generalized reduced gradient method (GRG), yielding deformations and internal forces of the suspension bridge. The proposed method demonstrates high computational efficiency and accuracy, achieving a computation time of less than 1 min for a 1700 m suspension bridge and a maximum vertical displacement error of only 0.59%. Further analysis reveals that the coupling effect between temperature and vehicle loads has a limited impact on vertical displacement calculations but causes a non-negligible influence on beam-end rotations. These findings highlight the necessity of considering temperature-vehicle interaction in suspension bridge assessment to ensure more precise and reliable predictions. |
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ISSN: | 1369-4332 2048-4011 |
DOI: | 10.1177/13694332251353620 |