Probabilistic study of chloride-induced corrosion of carbon steel in concrete structures
Bibliographic record
Abstract
This paper presents an approach for the probabilistic modeling of the chloride-induced corrosion of carbon steel reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of carbon steel, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and threshold chloride level for onset of corrosion. The parameters of the models are modeled as random variables and the distribution of the corrosion time and probability of corrosion are determined by using Monte Carlo simulation. The approach is applied to predict the level of corrosion in the top layer of reinforcing carbon steel of a highway bridge deck that was exposed to chlorides from deicing salts for forty years. The statistics of the governing parameters are generated from a detailed field survey using non-destructive evaluation methods. The predictions of the proposed model agree very well with the field data, which illustrates the capability of probabilistic models to characterize the corrosion response of carbon steel and the actual condition of reinforced concrete structures.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".