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Use of recycled glass powder to improve the performance properties of high volume fly ash-engineered cementitious composites

2017· article· en· W2774218974 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConstruction and Building Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPortlanditeMaterials scienceFly ashPortland cementComposite materialGlass recyclingFlexural strengthCuring (chemistry)CementitiousCompressive strengthCementDuctility (Earth science)

Abstract

fetched live from OpenAlex

High-volume fly ash (HVFA) Engineered Cementitious Composites (ECC) show reduced strength and physical properties, especially at early curing ages. The goal of this study was to improve their strength characteristics by incorporating recycled glass powder (RGP) for enhanced material sustainability. Composites containing 15, 30, 45 and 60% RGP as a replacement for FA, with FA to cement ratio of 2.2, were studied in HVFA-ECC. Standard ECC mixtures with FA to Portland cement (FA/PC) ratio of 1.2, and HVFA-ECC with FA/C ratio of 2.2 without RGP were also produced as control mixtures. The experimental results confirmed that incorporating RGP into HVFA-ECC significantly improves compressive and flexural strengths, chloride ion resistance and electrical resistivity, and results in a comparable ductility to standard ECC, based on FA to cement ratio of 1.2. Furthermore, self-healing of HVFA-ECC was accelerated and final recovery rate of strength and physical properties improved. Microstructural analysis showed high portlandite consumption and the formation of low Ca/Si ratio in new C-S-H structures near C-Na-Al-S-H as the main outcome of the binary admixture in ECC.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.207
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it