Load Distribution Factors for Curved Concrete Slab-on-Steel I-Girder Bridges
Bibliographic record
Abstract
The use of complex interchanges in modern highway urban systems have increased recently in addition to the desire to conform to existing terrain; both have led to increase the demand for horizontally curved bridges. One type of curved bridges consists of composite concrete deck over steel I-girders which has been the preferred choice due to its simplicity in fabrication, transportation and erection. Although horizontally curved steel bridges constitute roughly one-third of all steel bridges being erected today, their structural behavior still not well understood. Due to its geometry, simple presence of curvature in curved bridges produces non uniform torsion and consequently, lateral bending moment (warping or bi-moment) in the girder flanges. The presence of the lateral bending moments would significantly complicate the analysis and the design of the structure. Hence, a parametric study is required to scrutinize a simplified method in designing horizontally curved steel I-girder bridges. A parametric study is conducted, using the finite-element analysis software "SAP2000", to examine the key parameters that may influence the load distribution on the curved composite steel girders. Based on the data generated from the parametric study, sets of empirical equations are developed for the moment and shear distribution factors for straight and curved steel I-girder bridges when subjected to the Canadian Highway Bridge Design Code (HCHBDC) truck loading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".