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Evaluation of the SR Portland cement against sodium and magnesium sulfate attack: a physical and comparative analysis of mortars

2018· article· en· W2892523837 on OpenAlexaff
Diego Jesus De Souza, Marcelo Henrique Farias de Medeiros, Juarez Hoppe Filho

Bibliographic record

VenueRevista IBRACON de Estruturas e Materiais · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
FundersUniversidade Federal do ParanáCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade Estadual de Ponta Grossa
KeywordsSulfateMagnesiumMortarCementUltimate tensile strengthPortland cementMaterials scienceDurabilityCompressive strengthSodium sulfateComposite materialSodiumChemical compositionMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Sulfate attack is a term used to describe a series of chemical reactions between sulfate ions and hydrated compounds of the hardened cement paste. Thus, the chemical composition of the binders used is important for the durability of the structure against this aggressive agent. The objective of the present research is to evaluate the influence of sodium and magnesium sulfates on physical properties (linear expansion, flexure tensile strength, and compressive strength) of mortars composed by SR cement (CP V - ARI RS), as commercially sold in Brazil. The results indicate that SR cement complies the requirements established by Brazilian standards, as to chemical composition and resistance to sodium sulfate. However, for magnesium sulfate, SR cement was harmful to mortars mainly in mechanical.

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 score0.451
Threshold uncertainty score0.438

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 head0.045
GPT teacher head0.316
Teacher spread0.271 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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