Direct measurement of dynamics in road bridges using a bridge weigh-in-motion system
Bibliographic record
Abstract
A method is presented of measuring a bridge’s characteristic allowance for dynamic interaction in the form of Assessment Dynamic Ratio. Using a Bridge Weigh-in-Motion system, measurements were taken at a bridge in Slovenia over 58 days. From the total observed traffic population, 5-axle trucks were extracted and studied. The Bridge Weigh-in-Motion system inferred the static weights of the trucks, giving each measured event’s dynamic increment of load. Theoretical simulations were carried out using a 3-dimensional vehicle model coupled with a bridge plate model, simulating a traffic population similar to the population measured at the site. These theoretical simulations varied those properties of the 5-axle fleet that influence the dynamic response; simulating multiple sets of total (dynamic + static) responses for a single measured static strain response. Extrapolating the results of these theoretical simulations to a 50-year Assessment Dynamic Ratio gives similar results to those obtained by extrapolating the data measured using the Bridge Weigh-in-Motion system. A study of the effect of Bridge Weigh-in-Motion system errors on the predictions of Assessment Dynamic Ratio is conducted, identifying a trend in the Bridge Weigh-in-Motion calculations of maximum static response. The result of this bias is in turn quantified in the context of predicting characteristic maximum total load effect.
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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 | 0.001 | 0.001 |
| 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".