Durability of superficially treated concrete to ammonium sulfate and sulfuric acid solutions combined with cyclic environments
Bibliographic record
Abstract
Protecting concrete surface layer is essential for maintaining durability and functionality of concrete infrastructure during their service life. This thesis aimed to investigate the performance of nano-based coatings as superficial treatments for concrete elements exposed to aggravated exposures of ammonium sulfate, commonly found in agricultural, mining, and industrial zones, and sulfuric acid, usually found in wastewater and industrial facilities. Silane (hydrophobic agent) and vinyl ester (membrane-forming polymer) were used as base resins, in which nano-clay and nano-calcium carbonate particulates were dispersed at different dosages (0, 2.5, and 5%). In addition, a colloidal silica solution was used as a surface treatment. These surface treatments were applied to concrete specimens with water-to-binder ratios (w/b) of 0.6 (representing deteriorated concrete) and 0.4 (representing good quality concrete). Firstly, the durability of coated concrete specimens was evaluated under two ammonium sulfate exposures: full immersion and combined with cyclic environmental conditions (freezing-thawing and wetting-drying cycles). Degradation of concrete specimens was monitored by visual assessment and quantified in terms of mass and length changes over time. Secondly, the performance of superficially treated concrete specimens was assessed under two sulfuric acid exposures: full immersion and wetting-drying (W/D). Deterioration of concrete specimens was monitored by visual assessment and quantified in terms of mass changes over time. Furthermore, the degradation mechanisms and coatings’ performance were studied by thermal, mineralogical, and microstructural analyses. The results showed that colloidal silica could not adequately protect concrete specimens from these exposures. In addition, neat silane did not provide sufficient protection for concrete under sulfuric acid exposures; however, adding nanoparticles to neat silane significantly enhanced its performance under such exposures. On the other hand, silane/nanocomposites improved the durability of concrete under ammonium sulfate exposures, and superior performance was generally observed for concrete specimens coated with vinyl ester/ nanocomposites by the end of all exposures.
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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 | 0.000 | 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) | 0.001 | 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".