Probabilistic Modeling of Single and Concurrent Truckloads on Bridges
Bibliographic record
Abstract
The maintenance of bridges and the evolution of an appropriate bridge rating system require the consideration of loads from heavy trucks. These loads can arise from a single overweight truck or multiple trucks simultaneously present, or concurrent, on a bridge. This paper presents a probabilistic modeling approach to assess the frequency and likelihood of observing various bridge loads caused by single and concurrent trucks. The approach used weigh-in-motion (WIM) data collected at or near bridges of interest to identify single and concurrent trucks and their Corresponding loads. The modeling approach was applied to bridges near three WIM stations in Florida. Results showed that in any given month, there was a 100% probability of observing at least one single or concurrent truckload that exceeded twice the minimum weight of a single overweight truck (i.e., exceeded 711,715 N or 160,000 lb). In addition, the probability of observing extreme truckloads was significantly higher when all trucks were considered, as opposed to only overweight trucks. The modeling approach can easily be adapted to the goals of the study and to any region where WIM data are available at or near the bridge(s) of interest. Results generated from the modeling approach provide probabilistic loading input for bridge maintenance planning and truck permitting policy.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".