Traffic load modelling and factors influencing the accuracy of predicted extremes
Bibliographic record
Abstract
Design and assessment of highway bridges requires accurate prediction of the extreme load effects expected during the proposed or remaining life of the structure. Traditionally these effects are calculated using conservative codified deterministic loading models. While this conservatism is relatively insignificant in design, it may be critical in assessment. Advances in weigh-in-motion (WIM) technology, i.e., the process of weighing trucks travelling at full highway speeds, have increased the availability of accurate and unbiased site-specific traffic records. Assessments performed using WIM data are generally accepted as less conservative than those performed using generalized codified loading models. This paper briefly describes traffic simulation using WIM statistics. The implications of the accuracy of the recorded data and the duration of recording and of the sensitivity of the extreme to the method of prediction are investigated. Traffic evolution with time is also explored. The conclusions are of interest to engineers performing assessment of existing bridges.Key words: bridge, load effects, characteristic values, simulation, traffic flow, Monte Carlo, weigh-in-motion.
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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 | 0.003 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".