Influence of Mixture Compositions on Impact Resistance and Mechanical Properties of Concrete Cured in Cold Temperature Conditions
Bibliographic record
Abstract
This study aimed to present the effect of mixture proportions (including coarse-to-fine aggregate ratio and water-to-binder ratio) and mixture compositions (including the addition of different supplementary cementing materials and fibers) on the impact resistance and mechanical properties of concrete cured in cold temperatures. The impact resistance was evaluated using drop-weight and flexural impact tests conducted on concrete samples cured in different curing conditions. The studied parameters included coarse-to-fine aggregate (C/F) ratio (0.7 and 1.2), water-to-binder (w/b) ratio (0.4 and 0.55), type of supplementary cementing materials (SCMs) [20% metakaolin (MK) and 10% silica fume (SLF)], and the addition of steel fibers (0.35%) (SFs). The studied mixtures were cured under different curing conditions, including moisture condition at 23°C, air condition at 23°C, +5°C curing condition, and −10°C curing condition. The positive effect of using SFs and SCMs (MK and SLF) on enhancing the impact resistance and splitting tensile strength (STS) was more pronounced in samples cured at normal curing temperatures compared to samples cured at low temperatures. The mechanical properties and impact resistance of mixtures developed with higher C/F and w/b ratios were more affected by the low-temperature curing condition compared to the control mixture (with lower C/F and w/b ratios). The results also showed that low-temperature curing had a more pronounced negative effect on the impact resistance and STS than the compressive strength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".