Failure Analysis of Continuous Highway Steel I-Multigirder Bridges under the Combined Effect of Fatigue and Overloading Events
Bibliographic record
Abstract
A significant number of recent highway bridge failures have been attributed to the lack of financial resources that constrained owners to keep bridges in service under undesired circumstances. Given that many highway bridge failures were related to cyclic fatigue and overloaded trucks, or some combination thereof, the objective in this paper is to present an approach to assess the reliability of continuous highway steel I-multigirder bridge superstructures under the combined effects of fatigue damage and overloading. To that effect, Monte Carlo simulations were run for 42 short to medium-length steel I-multigirder bridges having configurations representative of bridges in North America. Fatigue damage locations, permanent loads, truck gross weights, and axle configurations were assumed to be random variables. The fatigue model used is consistent with the model implemented during calibration according to AASHTO’s specifications for a 75-year design life. Simple equations were constructed to quantify the probability of bridge system collapse resulting from the combined effects of fatigue and overloading events for typical continuous steel I-multigirder bridge superstructures. Accordingly, the expected service life of bridges would reduce by about 7 years if the percentage of overweight trucks increases from the national average of 5.1% to 12.0%. The proposed model can eventually be implemented in bridge management systems to account for the effects of truck loads, complementing these systems’ current focus on bridge condition ratings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".