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Record W4403680032 · doi:10.1680/jadcr.24.00050

Mechanical, physical and durability performance of engineered cementitious composites prepared with calcium aluminate cement

2024· article· en· W4403680032 on OpenAlexaff
Shahin Zokaei, Hocine Siad, Mohamed Lachemi, Obaid Mahmoodi, Emircan Özçelikci, Mustafa Şahmaran

Bibliographic record

VenueAdvances in Cement Research · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDurabilityCementMaterials scienceCementitiousComposite materialAluminateCalciumMetallurgy

Abstract

fetched live from OpenAlex

The aim of this research is to fill a gap in existing studies by exploring the use of calcium aluminate cement (CAC) as an eco-friendly substitute for ordinary Portland cement (OPC) in engineered cementitious composites (ECCs). The study investigates the impact of CAC, both with and without the presence of fly ash (FA), on the mechanical, physical, durability and microstructural properties of ECC–CAC mixtures. Various proportions of FA to CAC up to 1.5 were considered, while assessing different engineering parameters of compressive and flexural strengths, ductility, ultrasonic measurements, chloride penetrability and drying shrinkage. Moreover, scanning electron microscopy coupled with energy dispersive X-ray analysis and X-ray diffraction were used to analyse the reaction products related to CAC and/or FA in selected mixtures. Higher strengths of CAC–ECC were achieved at earlier curing ages, although the transformation of CAC’s metastable phases led to reduced strengths in ECC–CAC mixtures compared to the control ECC during extended curing periods. Furthermore, the combination of FA with CAC played a crucial role in avoiding the hydrates conversion and minimising the shrinkage in CAC–ECC, achieving even lower shrinkage levels than those observed in control OPC–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.

How this classification was reachedexpand

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)0.001
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 score0.181
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.329
Teacher spread0.305 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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