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Record W2955499025 · doi:10.3390/ma12132151

Evaluation of Engineering Properties of Calcium Sulfoaluminate Cement-based Concretes Reinforced with Different Types of Fibers

2019· article· en· W2955499025 on OpenAlexaff
Vahid Afroughsabet, Luigi Biolzi, Sara Cattaneo

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceCementComposite materialEttringiteFlexural strengthShrinkagePortland cementPolyvinyl alcoholMicrostructureCuring (chemistry)FiberCarbonationFiber-reinforced concreteHardening (computing)Properties of concreteLayer (electronics)

Abstract

fetched live from OpenAlex

emissions, as compared to that of the ordinary Portland cement (OPC). This paper evaluates the impact of different types of fibers on the engineering features of CSA-based concretes at different water-cement ratios of 0.35 and 0.28. In this study, metallic fibers including double hooked-end steel fibers and hooked-end steel fibers, and non-metallic fibers (i.e., polyvinyl alcohol (PVA) fibers) were utilized at fiber content of 1%. The mechanical properties of concretes were assessed at different curing ages. Dimensional stability of the concrete mixes was also examined. The morphology of the fractured specimens was studied by using the SEM method. The results indicate that the engineering properties of concrete were improved by introducing fibers to the concrete, irrespective of fiber type. The results show that DHE steel fiber has an important effect on the flexural performance of CSA cement-based concretes and results in deflection-hardening behavior. It was observed that fibers and particularly PVA fibers cause a decrease in shrinkage deformation. Microstructure tests demonstrate that prismatic ettringite is the main hydration product of CSA cement-based concrete. The SEM observation also confirms that the inclusion of CSA cement in concrete improves the cohesiveness between the fibers and cement matrix.

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 score0.003
Threshold uncertainty score0.975

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)0.0010.000

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 head0.029
GPT teacher head0.237
Teacher spread0.208 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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