Durability and Self-Sealing Examination of Concretes Modified with Crystalline Waterproofing Admixtures
Bibliographic record
Abstract
Repairing concrete structures costs billions of dollars every year all around the globe. For overcoming durability concerns and creating enduring economical structures, chemical admixtures, as a unique solution, have recently attracted a lot of interest. As permeability of a concrete structure is considered to play a significant role in its durability, Permeability Reducing Admixtures (PRA) is one of the ideal solutions for protecting structures exposed to water and waterborne chemicals. Different products have been developed to protect concrete structures against water penetration, which, based on their chemistry, performance, and functionality, have been categorized into PRA. As it has previously been tested by authors and proven to be a promising solution, a hydrophilic Crystalline Waterproofing Admixtures (CWA) has been considered for this study. This paper aims to investigate how this product affects concrete's overall freeze-thaw resistance, self-sealing, and corrosion resistance. Various testing methods have been utilized to examine the performance of CWA mixtures, including the linear polarization resistance, resonance frequency testing, half-cell potential, and self-sealing test. The reinforcement corrosion potential and rate measurements indicated superior performance for CWA-treated samples. After being exposed to 300 freeze-thaw cycles, concrete mixes containing CWA-even non-air-entrained ones-showed a Durability Factor (DF) of more than 80% with no signs of failure, while non-air-entrained control samples indicated the lowest DF (below 60%) but the greatest mass loss. The major causes are a reduction in solution permeability and lack of water availability in the concrete matrix-due to the presence of CWA crystals. Furthermore, evidence from the self-sealing test suggests that CWA-treated specimens can seal wider cracks and at a faster rate.
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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".