Elsevier Live load model for highway bridges *
Bibliographic record
Abstract
Abstract. Load models are developed for highway bridges. The models are based on the available statistical data on dead load, truck loads and dynamic loads. The paper deals mostly with the static live load. The model is derived from truck surveys, weigh-in-motion measurements and other observations. Simple span moments, hears and negative moments are calculated for various pans. Extreme 75 year loads are determined by extrapolation. The important parameters also include girder distribution factors and multiple presence (more than one truck on the bridge). Multiple presence isconsidered in lane and side-by-side with various degrees of correlation between truck weights. The maximum load is calculated by simulations. The developed live load model served as a basis for the development of new design provisions inthe United States (LRFD AASHTO) and Canada (Ontario Highway Bridge Design Code). Key words: highway bridges; live load; multiple presence; truck load.
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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 | 0.000 | 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 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".