Reliability-Based Prediction of Chloride Ingress and Reinforcement Corrosion of Aging Concrete Bridge Decks
Bibliographic record
Abstract
This paper presents a probabilistic approach for predicting the chloride contamination of concrete and reinforcing steel corrosion, which takes into account the uncertainty associated with the analytical models of chloride transport, corrosion initiation, as well as damage accumulation, material properties, structural dimensions, and applied environmental and mechanical loads. The proposed approach is illustrated on an aging reinforced concrete bridge deck that has been exposed to chlorides from deicing salts for forty years. An extensive non-destructive and destructive evaluation of the corrosion-damaged deck was undertaken. The field survey data showed a considerable level of variability in all parameters measured with coefficients of variation ranging from 34% for the concrete cover depth to 86% for the diffusion coefficient. The distributions of the chloride concentration at the level of the top reinforcement mat and the time for its corrosion initiation were generated using Monte Carlo Simulation. The simulated results were very close to the field data, which illustrates the prediction capabilities of probabilistic methods as opposed to deterministic methods.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".