Sensitivity of Reliability Index of Bridge Girders to Random Variables and Average Daily Truck Traffic
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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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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