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Record W2738025765 · doi:10.1139/cjce-2016-0491

Performance evaluation of foaming agents in cellular concrete based on foamed alkali-activated slag

2017· article· en· W2738025765 on OpenAlexvenueno aff
Azadeh Tarameshloo, Ebrahim Najafi Kani, Ali Allahverdi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsCompressive strengthMaterials scienceFoaming agentCuring (chemistry)Composite materialSlag (welding)CementGround granulated blast-furnace slagHydrogen peroxidePorosityChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, three different foaming agents were selected and their performance on density, compressive strength, pore structure, and molecular structure of alkali-activated blast furnace slag cellular concrete have been investigated. For this purpose, pre-formed foams based on sodium lauryl sulfate, protein-based foaming agent, and hydrogen peroxide were added to the alkali-activated slag paste with determined activator composition. After curing, density and compressive strength of cellular concretes were evaluated. Also, macroscopic pore size distribution was investigated by image processing technique for studying its relation with density and compressive strength. Results showed that with increasing the amount of foam, the density and the compressive strength decreased due to increases in both the number of pores per area and the pore average size. Samples containing protein-based foam showed higher mechanical strength, which could be due to its effect on the molecular structure of hydration product resulting in a stronger bond and hence higher compressive strength.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.254
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations25
Published2017
Admission routes1
Has abstractyes

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