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Degree of Hardening of Epoxy-Modified Mortars without Hardener in Tropical Climate Curing Regime

2015· article· en· W2242197106 on OpenAlexaff
Nur Farhayu Ariffin, Mohd Warid Hussin, Abdul Rahman Mohd Sam, Muhammad Aamer Rafique Bhutta, Nor Hasanah Abdul Shukor Lim, Nur Hafizah A. Khalid

Bibliographic record

VenueAdvanced materials research · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEpoxyMaterials scienceCuring (chemistry)Composite materialMortarCementHardening (computing)Flexural strengthCompressive strength

Abstract

fetched live from OpenAlex

Previous studies show that the epoxy resin will harden in the presence of calcium hydroxide from cement hydration process under steam curing. In this study, commercially available epoxy resin without any hardener was used as a polymeric admixture to prepare epoxy-modified mortars subjected to dry, and 5 day wet followed by dry curing in tropical environment. The mortars were prepared with a mass ratio of cement to fine aggregate 1:3, water-cement ratio of 0.48 and epoxy content of 5, 10, 15 and 20% of the cement. The tests conducted were workability, setting time, compressive strength, flexural strength, and degree of hardening of epoxy resin. The results of the study show that the optimum epoxy content that produced the highest strength was 10% under wet-dry curing. However, the degree of epoxy hardening starts to decrease with an increase in epoxy content above 10%. It was also found that a significant improvement in strength development is achieved along with additional dry curing period due to gradually hardening reaction of epoxy resin with cement hydrates.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.124
GPT teacher head0.371
Teacher spread0.246 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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