Life-cycle assessment of highway bridges
Bibliographic record
Abstract
Many transportation agencies have recently adopted bridge management systems (BMSs) tooptimize decisions related to the expenditure of their limited funds on bridge maintenance,rehabilitation, and replacement (MR&R). The analysis in most of these BMSs is based on thelife-cycle assessment of different MR&R alternatives for a network of bridges. This assessmentrequires reliable prediction of the condition of different bridge components when various MR&Ractions are implemented (including the 'do nothing' option). The state-of-the-art systemsconsider the use of the stochastic Markov chain models in predicting the bridge performance.This stochastic approach can predict the condition of a bridge component at any time usingpredefined transition probabilities obtained by expert judgment elicitation procedures. In thispaper, the actual condition data obtained from the Ministére de Transport du Québec (MTQ) areused to develop transition probabilities for concrete bridge decks. These probabilities areemployed to validate those developed based on the literature and engineering judgment. Inaddition, a subset of the data is utilized to validate the state independence assumption of Markovchains.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".