Effect of Self-Curing Admixture and Sand Type on the Mechanical and Microstructural Properties of Concrete in Hot Climate Conditions
Bibliographic record
Abstract
The performance of concrete in hot and arid regions, where summer temperatures typically range between 40 and 50℃, is critically affected by the rapid evaporation of mix water.This study systematically investigates the influence of elevated temperatures characteristic of these climates on both the fresh and hardened properties of concrete, with a focus on formulation variables.Three distinct sand types-calcareous, silicocalcareous, and siliceous-were utilized in conjunction with superplasticizers and curing agents to discern their effects under simulated hot weather conditions.These conditions replicated an ambient temperature of 50℃ for dry materials and water, a wind speed of 12 km/h, and a relative humidity of 10%, to emulate the average shaded environment in desert regions.Workability and compressive strength were evaluated, alongside a microstructural analysis conducted via Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD).It was observed that mixtures containing siliceous or silicocalcareous sands exhibited enhanced fluidity, while those with calcareous sand demonstrated superior compressive strength.Microstructural examinations revealed a denser matrix in the calcareous sand-based concrete when compared to its counterparts.Notably, the incorporation of curing compounds and superplasticizers was found to augment the compressive strength, particularly in calcareous sand mixtures, under hot weather conditions.This research offers critical insights into optimizing concrete formulations to mitigate the adverse effects of hot weather concreting, providing a valuable resource for concrete technologists in similar climatic zones.
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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".