The effects of global climate change on carbonation induced corrosion of reinforced concrete structures
Bibliographic record
Abstract
There is nearly unanimous consensus amongst scientists that increasing greenhouse gas emissions, including CO₂ generated by human activity, are affecting the Earth‘s climate. Climate change has the potential to overwhelm existing capacities, as well as durability of concrete infrastructure. Carbonation of concrete occurs due to a reaction between atmospheric CO₂ and the hydrated phases of concrete, leading to a drop in its pH and the depassivation of embedded rebar. Therefore, increases in carbonation rates of reinforced concrete structures are expected as a result of increased temperatures and CO₂ concentrations, with the enhanced risk of carbonation induced corrosion likely affecting the longevity of our concrete infrastructure. This thesis considered the potential consiquences of global climate change on our concrete infrastructure, with the objective being to determine if there is an increased risk of deterioration due to carbonation induced corrosion. A unique numerical model was developed to determine carbonation rates in structures, and verified through experimental tests. The model was applied to a number of cities in locations throughout the world to determine where structures were most vulnerable. Additionally, a number of other laboratory experiments were carried out to supplement the numerical model and provide insights as to how carbonation progress can be monitored within a structure. Using the model developed, and inputting forecasts for increases in future atmospheric CO₂ concentrations and weather conditions, it was shown that for medium quality, non-pozzolonic concrete in geographic areas where carbonation induced corrosion is a concern, global climate change will affect its progress in our concrete infrastructure. We will see much higher ultimate carbonation depths in the long term. The use of non-destructive testing (NDT) methods, and structural health monitoring (SHM) techniques could be invaluable in monitoring the progress of carbonation in a structure, but the data generated by the methods and techniques used must be analyzed carefully before making any conclusions. For the NDT methods and carbonation pH sensors which were evaluated in this study, it was found that ambient test conditions had a major impact on results.
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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".