Bridge Concept Design Using Heuristic Fuzzy Optimum Design and FEM
Bibliographic record
Abstract
In this study the aim is to present results of bridge concept design using heuristic fuzzy optimum design and FEM. The bridge concept is chosen as the basic suspension type. The deck plate rests on four supports. The middle supports are towers with suspension cables to lift up the bridge plate for minimising its deflection and bending stresses. Mass distribution load and flutter loading act on the plate. Geometric design variables are topology and dimensions of cables and deck. Material variable options are low strength and high strength steel. Decision variables are based on design variables. The main ones are cost and safety factors. The total goal is maximization of the fuzzy satisfaction of the user on all decision variables. The same optimal geometry is obtained for both steel options giving nearly equal performance. The softer steel option is preferable due lower cost. The model and FEM results agree reasonably in stresses and deflections. The fuzzy model used is shown to be an extension of probabilistic models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".