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ECO FRIENDLY CONCRETE FOR SUSTAINABLE STRUCTURES

2020· article· pt· W3114832370 on OpenAlexaff
Gustavo de Aguiar Isaia, Eduardo Rizzatti, Silvane Santos da Silva, Geraldo Cechella Isaia, André Lübeck

Bibliographic record

VenueMIX Sustentável · 2020
Typearticle
Languagept
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsImpact
Fundersnot available
KeywordsPhysicsEnvironmental scienceHumanitiesMaterials scienceArt

Abstract

fetched live from OpenAlex

The present article contemplates the study of eco friendly concrete by substituting Portland cement (PC) for very high levels of limestone filler (LF) in binary and ternary mixtures with fly ash (FA) in proportions of 50 to 80%, with ratios 0.25 a/b and optimization of the particle size. Results of compressive strength, CO2eq emission, energy consumption and binder intensity are presented, with which comparative indices were calculated to observe the performance of the mixtures. From the point of view of sustainability, it was possible to prepare concrete with a compressive strength of 51.8 MPa, at 91 days, with 77 kgCO2.m-3 of concrete, where 80% of the PC were replaced by 70% of LF and 10 % of FA, with consumption of only 97 kg.m-3 of PC (87 kg.m-3 of clinker) and 104 L.m-3 of water. The study shows the achievement ofstructural concrete with fck of up to 80 MPa with very low CO2eq emissions and energy consumption, through the use of high levels of mineral additions (MA) and reduced environmental impact.

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 score0.005
Threshold uncertainty score0.016

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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