Durability Monitoring for Improved Service Life Predictions of Concrete Bridge Decks in Corrosive Environments
Bibliographic record
Abstract
Abstract: The development of an effective strategy for the inspection and monitoring of the nation's critical bridges has become necessary due to aging, increased traffic loads, changing environmental conditions, and advanced deterioration. This article presents the development of a probabilistic mechanistic modeling approach supported by durability monitoring to obtain improved predictions of service life of concrete bridge decks exposed to chlorides. The application and benefits of this approach are illustrated on a case study of a reinforced concrete barrier wall of a highway bridge monitored over 10 years. It is demonstrated that service life predictions using probabilistic models calibrated with selected monitored field data can provide more reliable assessments of the probabilities of reinforcement corrosion and corrosion-induced damage compared to using deterministic models based on standard data from the literature. Such calibrated probabilistic models can help decision makers optimize intervention strategies as to how and when to repair or rehabilitate a given structure, thus improving its life cycle performance, extending its service life and reducing its life cycle cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".