Analysis and Design of Straight and Skewed Slab Bridges
Bibliographic record
Abstract
Results of an investigation aimed at determining bending moments and shear forces, required to design skewed concrete slab bridges using the equivalent-beam method are presented in this paper. Straight and skewed slab bridges were modeled using grillage and finite-element models to characterize their behavior under uniform and moving loads with the objective of determining the most appropriate modeling approach for design. A parametric study was carried out on 390 simply supported slabs with geometries covering one to four lane bridges of 3- to 20-m spans and with skew angles ranging from 0 to 60°. The analyses showed that nonorthogonal grillages satisfactorily predict the amplitude and the transverse distribution of longitudinal bending moments and shear forces, and can be used for the analysis of skewed slab bridges. Results of the parametric study indicated that shear forces and secondary bending moments increase with increasing skew angle while longitudinal bending moments diminish. Equations are proposed to include, as part of the equivalent-beam method for skew angles up to 60°, the increase of shear forces and the reduction of longitudinal bending moments. Equations are also given for computing secondary bending moments. A simplified approach aimed at determining the corner forces for straight and skewed bridges is proposed as an alternative to a more-refined analysis. The analyses indicated the presence of high vertical shear stresses in the vicinity of free edges that justifies suggesting to provide shear reinforcement along the slab free edges.
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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) | 0.000 | 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 | 0.000 | 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".