Influence of Climate Change on Probability of Carbonation-Induced Corrosion Initiation
Bibliographic record
Abstract
The consequences of climate change on infrastructure, particularly reinforced concrete (RC) bridges, have rapidly increased in recent years. These consequences are primarily driven by the surge in CO2 emissions, which significantly impacts the carbonation depth of RC structures. This study aims to investigate the probability of carbonation-induced corrosion initiation (PCICI) in RC bridge elements. To achieve this, the investigation incorporates a range of concrete covers, varying from 30 to 50 mm, and considers different concrete mixes with cement contents of 400, 350, and 250 kg/m3. The investigation utilizes the Monte-Carlo simulation method, considering different representative concentration pathways (RCPs) to account for two emission scenarios: RCP2.6 (low emission scenario) and RCP8.5 (high emission scenario). By analyzing projected CO2 concentrations and maximum temperature, the study provides insights into the potential corrosion initiation risks in RC bridges. The findings indicated a significant 66.3% increase in PCICI for a cement content of 250 kg/m3, compared to 400 kg/m3, under the RCP8.5 scenario, specifically when using a concrete cover of 30 mm by 2100. The study also revealed that the PCICI approached an approximate value of zero when concrete covers were set at 45 and 50 mm regardless of the variations in cement contents and the duration considered, for both the RCP2.6 and RCP8.5 scenarios.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".