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Record W2275276171 · doi:10.2991/icsmme-15.2015.51

Recycled concrete, a solution for fine and coarse raw material for new concrete

2015· article· en· W2275276171 on OpenAlexaff
Wilfrido Martínez Molina, Elía Mercedes Alonso Guzmán, Hugo Luis Chávez-García, Cindy Lara Gómez, F. M. Gonzalez Valdez, A A Torres Acosta, J T Perez Quiroz, H. Hernandez Barrios, W. Martinez Alonso

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsMinistry of Transportation of Ontario
FundersICA FoundationSecretaría de Educación PúblicaExxon Mobil Corporation
KeywordsDemolitionEnvironmental sciencePortland cementCarbonationWaste managementPollutionCementGeotechnical engineeringCivil engineeringEngineeringMaterials science

Abstract

fetched live from OpenAlex

Solid waste generation of Hydraulic Concrete is a new polluting of the earth.The most produced material in the world is the Portland Cement (PC), but its disadvantage lies on its requiring fossil fuels, as well as the CO x discharge to the atmosphere.Re-use of Hydraulic Concrete waste abates simultaneously a number of problems, as dumping of solid waste and CO x compounds into the environment; affectation of quarry sand and gravel stones, as well as endemic flora and fauna living in them; storm-water runoff by the inability of strata to filter demolished concrete contained in landfills.The use of crushed aggregates coming from Hydraulic Concrete demolition is used to generate Recycled Concrete, a material that can abate costs, diminish pollution, reduce the cost of building, and protect quarries from unnecessary exploitation.Nonetheless, development of Recycled Concrete faces the challenge of finding optimal designs to achieve the highest mechanical performance under static and dynamic stresses, the need to produce concretes less susceptible to carbonation, and therefore, to corrosion of reinforcing steel that protect with the concrete core, aside from waterproof and dense concrete.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.027
GPT teacher head0.237
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations0
Published2015
Admission routes1
Has abstractyes

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