Effect of different cement content and water cement ratio on carbonation depth and probability of carbonation induced corrosion for concrete
Bibliographic record
Abstract
Nowadays transportation infrastructure is subjected to a high percentage of carbon dioxide emissions. CO 2 greatly affects the carbonation depth of concrete, which can affect the deck for reinforced concrete bridges causing corrosion to steel reinforcement. Cement content and water to cement ratio greatly influence the carbonation depth of concrete. This study aims to investigate the effect of climate change on carbonation depth by considering different Representative Concentration Pathways [RCPs]. In addition, the effect of different compressive strengths on the carbonation depth was investigated in this research. Additionally, the effect of different cement contents on the probability of carbonation-induced corrosion has been investigated. Two parameters are considered, namely, the cement content 400 kg/m3, 350 kg/m3, and 250 kg/m3 and, the water to cement ratio [0.45 and 0.55]. This study RCPs for CO2 concentrations. The RCP [2.6, 4.5, 6, and 8.5] trajectory was used by the Intergovernmental Panel on Climate Change [IPCC], which represents low emission pathways, intermediate emission pathways, and high emission pathways, respectively. Carbonation depth has been estimated using Yoon’s and Stewart’s equations. Furthermore, the probability of carbonation-induced corrosion has been investigated using Monte Carlo simulation and the first-order reliability method at different cement contents for RCP 8.5. The percentage increase in the carbonation depth using Yoon’s compared to Stewart’s equations for concrete mixes which consist of different water to cement ratios and cement content for the years 2025 and 2100 for both RCP 2.6 and RCP 8.5 were calculated. Finally, the probability of carbonation-induced corrosion conducted by FORM for cement content of 250 kg/m3 has been increased by 18% compared to the probability of carbonation including cement content equal to 400 kg/m 3 for the year 2100.
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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 | 0.001 | 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".