The Effect of Mineral Additions on the Curing Process, Rheology and Durability of Heat Treated High Performance Concrete
Bibliographic record
Abstract
The demand for sustainable, high-performance construction materials has driven research into alternative cementitious materials. This study explores the potential of locally sourced Algerian andesite and calcined marl as supplementary cementitious materials (SCMs) in high-performance concrete (HPC). It examines their impact under heat-treated and non-heat-treated conditions, emphasizing pozzolanic reactivity and fresh and hardened state properties. Comprehensive characterization using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA) determined the chemical composition and microstructural attributes of these materials. The Sherbrooke mix design optimized HPC formulations. Partial cement replacement was carried out at levels of 10%, 20%, and 30% by mass, using andesite and calcined marl, with natural pozzolan serving as the reference mix. Experimental evaluations covered workability, ultrasonic pulse velocity (UPV), porosity, water absorption, and compressive strength. Advanced imaging techniques, particularly computed tomography, assessed internal microstructural evolution, while statistical modeling (Design of Experiments, DOE) provided deeper insights into performance optimization. Results confirm that andesite and calcined marl demonstrate good performance characteristics compared to the natural pozzolan reference. Fresh-state assessments showed favorable workability and stability, while hardened-state analyses revealed reduced porosity, denser microstructure, and satisfactory durability. Heat-treated samples exhibited notable early-age strength, benefiting precast and rapid construction. Non-heat-treated specimens, however, developed long-term durability through sustained pozzolanic activity. Sulfuric acid exposure confirmed the chemical resilience of these formulations, reinforcing their suitability for demanding environments. This research advances the valorization of local mineral resources for sustainable HPC, reducing reliance on imports. The combined benefits of thermal curing and pozzolanic synergy offer a durable, eco-friendly concrete solution. By bridging material characterization, performance optimization, and practical implementation, this study provides valuable insights for academia and industry, fostering resilient, resource-efficient construction methodologies
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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".