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Record W3194068443 · doi:10.11159/mmme21.117

Evaluation of the Mechanical Behavior of Mortars Obtained ByGeopolymerization of Calcined Clay and Demolition Mortar

2021· article· en· W3194068443 on OpenAlexvenueno aff
D.L. Mayta-Ponce, V.C. Bringas-Rodríguez, J.F. Gamarra-Delgado, M.L. Benavides-Salinas, Cris Katherin Palomino-Ñaupa, G.P. Rodríguez-Guillén, F.A. Huamán-Mamani

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
KeywordsMortarDemolitionMaterials scienceCalcinationComposite materialEngineeringCivil engineeringChemistry

Abstract

fetched live from OpenAlex

Geopolymeric mortars were prepared from a mixture of calcined clay powders (from demolition bricks), demolition mortar and a 12 molar alkaline hardening solution of sodium hydroxide.The geopolymeric mortars were compared physically, microstructurally and mechanically with their Portland cement counterparts.The results revealed similar densities between both types of mortars (geopolymeric and ordinary Portland cement).The microstructure was also similar in both mortars, two phases can be clearly identified, the continuous binder phase and the phase of individual fine sand particles dispersed within the continuous cement phase.Regarding the mechanical data, it could be verified that the mixture with 80 Vol.% of fine sand, 10 Vol.% of calcined clay and 10 Vol.% of demolition mortar was the one that showed the best mechanical results, with an average mechanical resistance of 34.5 MPa.However, the highest average mechanical strength value for geopolymeric mortars is below the average mechanical strength value of ordinary Portland cement mortar (50 MPa).

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicConcrete and Cement Materials ResearchFrench-language works237,207