Systematic data-driven meta-analysis of class F fly-ash geopolymer concrete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it