Influence of Various Crack Widths in RC Bridge Decks on the Initiation of Chloride-Induced Corrosion
Bibliographic record
Abstract
This study investigates the influence of crack width on the time to chloride-induced corrosion initiation in reinforced concrete (RC) bridge decks, incorporating climate change projections through the year 2100 under IPCC scenarios (RCP2.6 and RCP8.5). A probabilistic modelling approach using Monte Carlo simulations (MCSs) was applied to assess corrosion initiation across a range of environmental and structural conditions, including normal and high-performance concrete (HPC), varying concrete cover depths, and the use of supplementary cementing materials (SCMs). The results indicate that increasing the crack width significantly accelerates chloride ingress, reducing the time to corrosion initiation by up to 41% compared with that under uncracked conditions. HPC demonstrated superior durability, delaying corrosion initiation by nearly twice as long as normal concrete under identical chloride exposure. Elevated temperatures projected under high-emission scenarios further reduce service life by increasing chloride diffusion rates. Polynomial regression models were developed to relate crack width and concrete cover to corrosion initiation time, offering practical tools for durability-based design and service life prediction. These findings highlight the importance of enhanced crack control, climate-adaptive material selection, and updated durability standards to improve the resilience of RC bridge infrastructure in the face of climate change.
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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 | 0.000 | 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 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".