Redistribution of longitudinal moments in straight, continuous concrete slab – steel girder composite bridges
Bibliographic record
Abstract
The effect of loading and geometric parameters on the transverse and longitudinal redistribution of moments in continuous composite bridges, comprising a concrete slab on parallel steel girders, is investigated with the nonlinear finite element method. Fifty bridges are analyzed over their entire range of loading up to failure, and their moment redistribution factors are determined and compared with the relevant predictions of the Canadian Highway Bridge Design Code (CHBDC) and the AASHTO LRFD Bridge Design Specifications. The parameters studied included truck position along the bridge, number of loaded lanes, bridge width, number of girders, slab thickness, degree of composite action, and presence of diaphragms. The study reveals that among the preceding parameters only the number of loaded lanes and the bridge width significantly affect transverse redistribution of moments at ultimate limit state (ULS). However, most of the preceding parameters affect longitudinal redistribution at ULS. Finally, it is demonstrated that plastic analysis of composite multi-girder continuous bridges, treated as an equivalent beam, provides a reasonable estimate of their longitudinal moment redistribution capacity at ULS. It is demonstrated that the actual load-carrying capacity of a composite bridge may be more than 50% higher than that predicted by the CHBDC or AASHTO code. Such higher predicted capacity may obviate the need for retrofit in some cases.Key words: analysis, bridge, composite, concrete, distribution, finite element, inelastic, load, steel.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".