Influence of concrete cover thickness on steel corrosion in reinforced concrete: Insights from advanced imaging and material analysis
Bibliographic record
Abstract
This research manuscript presents a comprehensive analysis of the effects of cover thickness on the corrosion dynamics in reinforced concrete structures. Two distinct concrete cover thicknesses were studies namely: the small cover (SC) and the large cover (LC). Employing advanced 3D imaging techniques, X-ray Computed Tomography (CT), and material analysis methodologies, Scanning Electron Microscopy (SEM) equipped with Energy-Dispersive X-ray Spectroscopy (EDS) and Raman spectroscopy. This study delves into the corrosion process under accelerated conditions. The examination of point corrosion in both specimens highlights distinctive patterns influenced by the concrete cover thickness. The SC experiences point corrosion in larger pits within the corrosion zone of the steel reinforcement, resulting in interconnected 'wormholes' pathways that amplify localized corrosion effects. The LC displays a more dispersed pattern of point corrosion initiation within the corrosion zone of the steel reinforcement, with the resulting corrosion products remaining confined to their original locations. Analysis of crack formation reveals that SC exhibits an increase in cracks with branching, but their origins do not align with areas of maximum corrosion pit formation in the steel reinforcement. In contrast, LC displays a unique pattern of crack initiation, originating near pores and within the corrosion pits in the steel reinforcement, with smaller openings and limited branching, primarily guided by pore presence. The Raman spectroscopy analysis reveals that the predominant compounds in SC are iron hydroxides, primarily ferrihydrite with reduced crystallinity, indicating an ongoing corrosion process within the steel reinforcement. In contrast, the corrosion products in LC consist of iron oxides and iron hydroxides, reflecting a more complex corrosion process within the steel reinforcement.
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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 | 0.001 |
| 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".