Structural optimization of two-girder composite cable-stayed bridges under dead and live loads
Bibliographic record
Abstract
A large number of variables are involved in the optimization of cable-stayed bridges, which makes the optimization impractical when many load cases are considered. To reduce the number of variables to be optimized, a discrete phases approach for structural optimization is developed in this study. The approach couples the finite element method with the genetic algorithm optimization approach. The design variables are divided into two categories: (i) main variables: number of stay cables, I-girder inertia, concrete slab thickness, and tower dimensions; and (ii) secondary variables: I-girder dimensions, stay-cable areas, and pre-tensioning forces. Two design objectives are tested: (i) lightest deck mass; and (ii) lowest material cost. Three load cases are considered: (i) dead and truck plus lane live loads; (ii) dead and lane live loads; and (iii) dead load. The results show the importance of considering the truck loads in structural optimization and the efficacy of the phases approach for different objectives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".