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Use of waste glass in cement-based materials

2010· article· en· W3176459782 on OpenAlexaff
Rachida Idir, Martin Cyr, Arezki Tagnit‐Hamou

Bibliographic record

VenueEnvironnement Ingénierie & Développement · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAlkali–silica reactionMortarGlass recyclingPozzolanMaterials scienceCementReuseComposite materialGlass fiberAggregate (composite)Glass productionAlkali metalWaste managementMineralogyChemistryPortland cementEngineering

Abstract

fetched live from OpenAlex

Demand for recycled glass has considerably decreased in recent years, particularly for mixed-glass. Glass is cheaper to store than to recycle, as conditioners require expenses for the recycling process. There are several alternatives for the reuse of composite-glass. According to previous studies, all these applications, which require pre-conditioning and crushing, are more or less limited and unable to absorb all the quantities of waste glass available. In order to provide a sustainable solution to glass storage, a potential and incentive way would be to reuse this type of glass in concretes.Depending on the size of the glass particles used in concrete, two antagonistic behaviours can be observed: alkali-silica reaction, which involves negative effects, and pozzolanic reaction, improving the properties of concrete. The work undertaken here dealt with the use of fine particles of glass and glass aggregates in mortars, either separately or combined. Two parameters based on standardised tests were studied: pozzolanic assessment by mechanical tests on mortar samples and alkali-reactive aggregate characteristics and fines inhibitor evaluations by monitoring of dimensional changes. It is shown that there is no need to use glass in the form of fines since no swelling due to alkali-silica reaction is recorded when the diameter of the glass grains is less than 1mm. Besides, fine glass powders having specific surface areas ranging from 180 to 540m² / kg reduce the expansions of mortars subjected to alkali-silica reaction (especially when glass aggregates of diameters larger than 1 mm are used). La demande pour le verre recyclé a chuté considérablement au cours de ces dernières années plus particulièrement pour le verre mixte. Parce qu’entreposer le verre mixte revient moins cher que son recyclage, les conditionneurs exigent, depuis quelques années, des frais pour le recycler. Actuellement, plusieurs centres de tri refusent de payer des frais pour acheminer le verre mixte auprès des conditionneurs et par conséquent, la plupart d’entre eux accumulent ce type de verre.Plusieurs alternatives de réutilisation de verre mixte existent. Une fois conditionné, ce verre peut être utilisé dans la fabrication de la laine de verre, les filtrations municipales et de piscines, l’abrasion au jet …etc.D’après des études faites tous ces débouchés, qui nécessitent un pré-conditionnement, y compris un broyage, sont plus au moins limités et ne parviennent pas à absorber ces quantités de verre récupérées. Afin d’apporter une solution durable à la problématique de verre entreposé, une autre piste potentielle et encourageante, consiste à le valoriser dans les bétons.Une fois utilisé dans le béton et en fonction de sa grosseur, il a été montré que le verre peut conduire à deux types de comportement à conséquences complètements antagonistes : la réaction alcali-silice entraînant des effets néfastes et la réaction pouzzolanique bénéfique pour la structure.Ce travail, traite de l’utilisation des fines et des granulats de verre dans des mortiers, tantôt utilisés séparément tantôt combinés. Deux paramètres basés sur des essais normalisés ont été étudiés : l’évaluation de la pouzzolanicité par des essais mécaniques sur éprouvettes de mortiers et l’évaluation des caractères alcali-réactif des granulats et inhibiteur des fines par suivi de variations dimensionnelles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0060.000

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 head0.244
Teacher spread0.217 · 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.

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

Citations26
Published2010
Admission routes1
Has abstractyes

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