Effect of truck position and multiple truck loading on response of long-span metal culverts
Bibliographic record
Abstract
Long-span metal culverts have been used for almost 50 years as an economical alternative to short-span bridges. Current design methods are based on two-dimensional finite element analysis using beam theory to represent the structure, or three-dimensional analysis employing orthotropic shell theory. However, neither analysis has been used to investigate the most critical position for trucks at the surface of long-span metal culverts. This paper shows results of three-dimensional finite element analysis, employing orthotropic shell theory and explicitly modeling the geometry of corrugated plates for a specific box culvert tested using a fully loaded dump truck. The analysis was then extended to study the effect of truck position on the response of long-span box and arch culverts. The finite element models, employing orthotropic shell theory and explicitly modeling the geometry of corrugated plates, successfully produced the behaviour of the culvert under truck loading for different truck positions. Culvert deformations were calculated within 7%–13% of the measured values at different locations. The bending moment at the crown was within 4%–17% of the values calculated using the measured strains. If three-dimensional finite element analysis is used to design these culverts, two design trucks should be considered (current design considers a single design truck). The highest moment or thrust is obtained when the truck tandem axles are located above the crown of the culvert.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".