Shrinkage, Permeation and Freeze–Thaw Characteristics of Ambient Cured High Calcium-Based Alkali-Activated Engineered Composites
Bibliographic record
Abstract
polyvinyl alcohol (PVA) fibers, silica sand, binary or ternary combinations of precursors (fly ash class C 'FA-C', fly ash class F 'FA-F' and ground granulated blast furnace slag 'GGBFS') and two types of powder form alkaline reagents (Type 1 and Type 2) are evaluated compared to conventional engineered cementitious composites (ECCs) and alkali-activated mortars (AAMs) without fiber. AAECs developed satisfactory compressive strength ranging from 34 MPa to 46 MPa. Expansion/shrinkage and mass change (loss/gain) behaviors are affected by binary/ternary combination of source materials, reagent types and curing regimes (water or ambient) for both AAMs and AAECs. The binary (FA-C + GGBFS) and reagent 2 (calcium hydroxide + sodium sulfate) composites demonstrated lower shrinkage due to formation of crystalline C-A-S-H/C-S-H binding phases than their ternary (FA-C + FA-F + GGBFS) and reagent 1 (calcium hydroxide + sodium metasilicate) counterparts which formed amorphous N-C-A-S-H/N-A-S-H phases. The matrix densification due to the formation of reaction products and fiber-induced micro-confinement leads to lower shrinkage and mass change of AAECs compared to their AAM counterparts. Composites exhibited lower or comparable secondary sorptivity indices compared to control ECC, indicating their superior permeation performance. All AAECs had a relative dynamic modulus of elasticity (RDME) greater than 90% at 300 cycles (comparable to control ECC), exhibiting satisfactory freeze-thaw resistance with reagent 2 mixes showing better performance compared to those with reagent 1. The production feasibility of strain hardening AAECs with powder form reagents having satisfactory mechanical and durability properties is confirmed.
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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".