Existing Bridge Formulas for Truck-Weight Regulation from International Jurisdictions and Resulting Load Stresses on Single-Span Bridges
Bibliographic record
Abstract
This paper identifies and characterizes existing bridge formulas from international regions and presents the results of an analysis of the allowable gross vehicle weights and bridge load stress effects on single-span bridges resulting from these formulas. This is done to provide insight into this method of regulating truck size and weight to identify influencing factors and considerations for future decisions regarding the creation of new bridge formulas, or modification of existing ones. It is found that bridge formulas vary significantly in terms of the level of restrictiveness of allowable loads and imposed load effects due to the design criteria used in their development including truck configurations, bridge design methods, design loads, and allowable load rating. Bridge formulas should be designed to limit the imposed stresses on bridges based on criteria suitable to a jurisdiction’s truck fleet and infrastructure characteristics in order to adequately regulate truck sizes and weights. Many issues may result from the implementation of an unsuitable bridge formula for the infrastructure and transportation characteristics of a jurisdiction in terms of the design overstress criteria and additional axle spacing and weight limits. The unintended, and possibly undesirable, outcomes of implementation of a bridge formula must be monitored and resolved for safety, dynamic performance, and infrastructure impacts. With the continuously changing infrastructure and truck transportation characteristics, bridge formulas must be reevaluated and updated to ensure the adequacy of limit weights.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".