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Standardized Testing for Determining the Durability of High-Volume Fly Ash Mixtures

2012· article· en· W2022770605 on OpenAlexaff
Michelle Nokken, Tarek Salloum, Allen Michael Idle, Luis A. Martinez Ramos

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsFly ashDurabilityCompressive strengthSilica fumeMortarSulfateMaterials scienceChlorideAggregate (composite)CementComposite materialWaste managementMetallurgyEngineering

Abstract

fetched live from OpenAlex

Fly ash is becoming increasing used in concrete structures for enhanced durability. This research investigated two types of fly ash in conjunction with four cements. The cements were both high and low alkali, with and without blended silica fume. Mortar and concrete mixtures were prepared containing from 0 to 80% fly ash replacement. Standardized tests were performed for compressive strength, as well as sulfate, chloride, and alkali resistance. High replacement levels performed relatively well in sulfate and alkali resistance, but poorly in regards to compressive strength and chloride resistance. From a durability perspective, 40% fly ash replacement was found to have the best overall performance from these mixtures. Standardized test methods are comparative by nature, and as such cannot replicate the complexity of concrete mixtures and their exposure conditions; higher replacements have been successfully used in practice.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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