Structural Optimization of Cable-Stayed Bridges Considering the Action of Permanent and Transitory Loads
Bibliographic record
Abstract
Cable-stayed bridges are complex structures with several advantages such as aesthetical appeal, economic use of materials, and efficient construction method. Due to these advantages and the extensive knowledge gained from projects over the years, longer cable-stayed bridges are being constructed. As span lengths increase, structures become more flexible, which makes the accurate evaluation of wind loads critically important in the design of cable-stayed bridges. A large number of variables are involved in the design of cable-stayed bridges. Those include overall geometric dimensions, cross-sectional dimensions, number of stay-cables and pre-tensioning forces to be applied to the cables. Taking all variables into account, and considering the need to conduct multiple moving load analyses and to calculate accurately aerodynamic wind forces, a design optimization process for such bridges becomes challenging. In this thesis, a numerical model capable of achieving this design optimization task is developed. The numerical model uses a structural system in which the deck is composite steel-concrete with two I main girder. The developed numerical model is based on the Finite Element Method (FEM), the Real Coded Genetic Algorithm (RCGA), and the Discrete-Phases Design Approach. The latter classifies variables into two categories: (i) main variables: number of stay-cables, I-girder inertia, concrete slab thickness, tower cross-section external dimensions, tower height above the deck; (ii) secondary variables: I-girder dimensions, stay-cable areas and pre-tensioning forces. The main variables are design variables optimized directly by the RCGA, while the secondary variables are indirectly optimized by the discrete phases. Buffeting wind loads are considered as equivalent static forces, which were validated through a theoretical-experimental correlation. This powerful tool is used to assess the importance of considering truck versus lane loads, as well as wind buffeting loads and various aeroelastic instabilities in the design optimization process. Results show that the most critical load combination include the wind effect, and that the critical wind velocities of aeroelastic phenomena play a significant role for high values of basic wind speeds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".