Nonlinear Analysis of Cable-Supported Bridges Using an Open-source Finite Element Software
Bibliographic record
Abstract
It has been shown that the nonlinear differential equations representing the structural system of a suspension bridge exhibit nonlinear modal coupling. Mathematicians even demonstrated that such coupling could lead to large torsional vibrations of the bridge deck. It appears that such large oscillations from nonlinear modal coupling originate from geometric nonlinearities of the bridge structure. Since such nonlinear coupling could play a role in the stability of cable-supported bridges under wind effects, it is deemed necessary to develop a better understanding of the nonlinear behavior of cable-supported bridges. This was done using nonlinear finite analysis results of nine suspension bridges and two cable-stayed bridges with main spans ranging from 856 m to 4140 m. For this purpose, Code_Aster, an open-source finite element software, was utilized for the required numerical simulations. This paper therefore presents the authors’ experience with the development and usage of a framework for the nonlinear analysis of cable-supported bridges based on an open-source finite element software. At first, the advantages and disadvantages of using an open-source finite element software instead of a commercial one are discussed in the context of cable-supported bridges. Then, an overview of the analysis framework is provided, which includes the development of macro-commands for the calculation of cable preloads, nonlinear aerostatic analysis and nonlinear generalized stiffness analysis. This is followed by a presentation and discussion of typical results for the validation of the cable-supported bridge models and results of nonlinear analysis. Finally, a plan is outlined for future developments of the framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".