Load Distribution Factors of Simply Supported Concrete T-beam Bridges under Typical Freight Vehicle Loads
Bibliographic record
Abstract
Accurate evaluation of load distribution behavior is crucial to the safety evaluation and normal operation of short-and medium-span concrete girder bridges.In this study, 3D finite element analysis was performed to calculate the load distribution factors (LDFs) for a sample of reinforced concrete T-beam bridges under representative typical freight vehicles and the results were compared with those obtained by the American Association of State Highway and Transportation Officials (AASHTO) specification.The parameters that influenced the LDF, namely, transverse loading position, bridge span length, and vehicle type, were analyzed.Results demonstrate that the transverse loading position has a considerable influence on the LDF.The LDF of the interior girder decreases by 45% when the vehicle moves from the centerline of the bridge to the side of the barrier.For the 20 m T-beam bridge, the LDFs of the interior and exterior girders reach the maximum value in the allowable range of the vehicle transverse position, and with the increase in span length, LDF decreases gradually.Among all the loading vehicles, the three-axle truck has the largest LDF, which decreases with the increase in the number of axles.Compared with the LDF in the AASHTO specification, the LDF obtained by finite element analysis is reduced by 24.5%-40.3%,and this reduction can effectively improve the load rating level of bridges in service.The proposed method provides a valuable reference for the safety assessment of bridges in service, which can effectively avoid unnecessary maintenance and reconstruction of old bridges.
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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 | 0.002 | 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".