Bond Strength of Nano-Modified Concrete under Freezing Temperature using Hybrid Protection
Bibliographic record
Abstract
Concrete and steel rebars bond strength depends on multiple factors such as concrete-steel interface and development in mechanical properties of concrete. However, concrete cured under cold temperatures (below 5) suffers from insufficient hydration development of cementitious paste; consequently, the mechanical properties of concrete are negatively affected. In this paper, concretesteel bonding behaviour in concrete cast and cured under freezing temperature was explored. Three concrete mixtures were cast and cured at -20. The mixtures were protected using hybrid system consisting of insulation blankets and phase change materials mat. The mixtures comprised General Use cement, fly ash (20%), and nano-silica (6%) as well as calcium nitrate-nitrite as a cold weather admixture system. The mixtures were assessed in terms of internal temperature evolution, compressive and tensile strengths, as well as modulus of elasticity. In addition, the different mixtures' bond strength with steel re-bars was evaluated through a pull-out test. Thermogravimetric analysis was conducted to assess the hydration development of the adopted mixtures. Furthermore, to visualize the quality of the interface between steel-rebars and the different cementitious matrices, environmental scanning electron microscopy associated with energy dispersive x-ray analysis was performed on fracture pieces extracted from the interface. Nano-modified concrete protected using the adopted hybrid protection system produced good quality concrete-steel interface with adequate bond strength, without need for heating operations prior to casting and during curing under the adopted freezing temperature (-20); thus, it may present a viable option for cold weather structural applications.
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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".