Bridge evaluation by mean load method per the Canadian Highway Bridge Design Code
Bibliographic record
Abstract
The Canadian Highway Bridge Design Code (CHBDC) provides two alternative methods for evaluating the strength of existing bridges. The load and resistance factor method provides a general approach and covers the most extreme load situations that can occur in a general bridge population. The mean load method considers the uncertainties of loads acting on a specific bridge, the method of analysis, and resistance of the structure involved, and thus can provide a more accurate evaluation of individual bridges. Since traffic load represents a major portion of bridge loads, a better evaluation of specific bridges is obtained by using the statistical parameters of traffic loads observed on the structure. However, the overall accuracy depends heavily on capturing the most critical loading conditions during the survey periods. The mean load method is particularly valuable where actual traffic loads are expected to be significantly lower than those used in code calibration and when the potential economic benefits arising from a more realistic evaluation outweigh the extra costs of live load data collection and analysis. This paper demonstrates that the mean load method using site-specific traffic loading information can lead to a significantly higher live load-carrying capacity of a bridge.Key words: highway bridges, bridge evaluation, reliability, mean load method, bridge testing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".