Traffic load modelling and factors influencing the accuracy of predicted extremes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it