Effect of Activator Concentrations on the Postfire Impact Behavior of Alkali-Activated Slag Concrete
Bibliographic record
Abstract
Changing concrete ingredients significantly affects its performance due to changing the type of hydration products formed. The stability of these hydration products will dominate concrete impact behavior before and after exposure to fire. Limited research had explored the role of activators, as the main ingredient of alkali-activated slag concrete (AASC), on impact performance. Hence, this study highlights the effects of activator characteristics on the impact behavior of AASC at an ambient condition (23°C) and after exposure to elevated temperatures (200°C, 400°C, and 600°C). Conventional ordinary portland cement (OPC) concrete was also tested for general performance comparison. Besides the drop weight impact test, compressive and indirect splitting tensile strength, shrinkage, ultrasonic pulse velocity and water absorption tests were conducted to evaluate AASC performance. In addition, thermogravimetric analysis (TGA), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were used to confirm and analyze findings. Results confirmed the better impact performance of AASC compared to OPC concrete. Activator concentrations showed contrary effects on AASC performance at ambient and elevated temperatures. High activation levels improved strength and impact capacity at ambient temperature, showing lower internal defects and higher hydration product formation. Conversely, lowering the activation level at elevated temperatures was preferable and resulted in a higher residual strength and impact absorption capacity. This was ascribed to the high unreacted slag particle crystallization to akermanite at higher temperatures, leading to strength gain, fewer hydration products to decompose, and high microstructure ductility that accommodated the thermal incompatibility. Hence, designing AASC while focusing only on maximizing strength can be misleading based on the targeted performance and exposure conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".