Sensitivity of Reliability Index of Bridge Girders to Random Variables and Average Daily Truck Traffic
Bibliographic record
Abstract
The objective of the presented study is to evaluate the sensitivities of the random variables and Average Daily Truck Traffic (ADTT) on the evaluated reliability of bridges. The reliability analysis is carried out for the Strength I Limit State in the AASHTO LRFD Bridge Design Specification. The Weigh-In-Motion (WIM) data recorded at 24 WIM stations in Missouri are processed and used as an input to simulate realistic live loads due to truck traffic. Gumbel Type I extreme value distribution is used to represent daily maximum positive moments and extreme value theory is used to project the daily maximum values to the maximum values in 75 years of bridge lifespan. Sensitivity analysis is conducted to understand the relative effects of random variables and ADTT on the calculated reliability index. The result shows that the ADTT affects the reliability index as sensitively as other random variables, such as dead load, girder distribution factor, and dynamic impact factor. Hence, explicit consideration of uncertainties in ADTT is suggested for future calibration studies. Alternatively, the reliability index needs to be assessed conditioned on deterministic ADTT.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".