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Record W2791249715 · doi:10.1520/acem20160081

Thermal Conductivity of Hydrated Paste in Cement-Based Foam Microstructure

2018· article· en· W2791249715 on OpenAlexaff
Farnaz Batool, Vivek Bindiganavile

Bibliographic record

VenueAdvances in Civil Engineering Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMicrostructureComposite materialThermal conductivityCementConductivityThermalChemistry

Abstract

fetched live from OpenAlex

Abstract This article presents the thermal constants of hydrated cement paste, which constitute the air-void wall of cement-based foam microstructure, and examines its influence on moisture, age, pozzolanic admixture type, and content. Along with the reference mix, containing portland cement only, six other mixes were prepared by replacing cement with fly ash, silica fume, and metakaolin, up to 20 % by mass in the binder. The Transient Plane Source, a thermal analyzer that conforms to ISO test standards, was employed to evaluate the thermal constants. Here, the measurements were made at 60, 120, 210, and 300 days. The results revealed that the drop in thermal conductivity for the cement paste was significant in the earlier age but progressively became insignificant as the hydration advanced. It was also found that adding fly ash and silica fume in higher dosages resulted in reducing the conductivity, while metakaolin exhibits a reverse trend. Furthermore, the reduction in the moisture content due to pozzolanic admixture was also noticed. Finally, an empirical formulation was developed to predict the thermal conductivity of the hydrated cement paste, which was also validated against the results of past research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.233
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2018
Admission routes1
Has abstractyes

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