Systematic data-driven meta-analysis of class F fly-ash geopolymer concrete
Bibliographic record
Abstract
• 799 fly-ash geopolymer mixes normalized to 150 × 300 mm cylinder strength • Dataset bundles chemistry, curing, multi-age strengths and CO₂ in one file • Optimal range: 10–12 M NaOH, silica modulus 1.5–2.3, curing ≤ 75°C • Design charts halve CO₂ versus OPC for ≥50 MPa structural concretes Geometry-dependent strength reporting has hindered reliable design of Class F fly-ash geopolymer concrete (FA-GPC). In this study, compressive-strength (CS) results from about 800 mixtures were normalized to the reference 150Ø300 mm cylinder, permitting direct cross-comparison of specimen geometries extracted from 67 peer-reviewed papers. The harmonized dataset bundles oxide chemistry, mix proportions, activator composition, curing schedules, fresh-state metrics, multi-age strengths, and cradle-to-gate CO₂ inventories. Normalized CS across 1–365 days spans 5.8–85.0 MPa, and CO₂ intensities range 66–895 kg CO₂/m³ (mean 160 ± 91). Data mining isolates practical activation windows—NaOH 10–12 M, silica modulus 1.5–2.3, curing ≤ 75°C—that consistently deliver 28-d CS ≥ 50 MPa at ∼160 kg CO₂/m³. Compared to strength-matched OPC concretes, these mixes reduce embodied carbon by ∼45–55% (median ≈ 50%). Strength–carbon design maps and the open dataset enable practitioners to target structural classes under explicit CO₂ budgets and provide a reproducible springboard for machine learning-based prediction, life-cycle assessment, and optimization of alkali-activated concretes and geopolymers.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".