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Record W3123370588 · doi:10.1139/cjce-2020-0343

Chemical attack on concrete containing a high volume of crumb rubber as a partial replacement for fine aggregate in engineered cementitious composite (ECC)

2021· article· en· W3123370588 on OpenAlexvenueno aff
Wesam Salah Alaloul, Muhammad Ali Musarat, Sani Haruna, Bassam A. Tayeh, Muhammad Nurzahin Bin Norizan

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsCrumb rubberMaterials scienceScrapAggregate (composite)Composite materialNatural rubberVolume (thermodynamics)Compressive strengthCementitiousDispose patternDurabilityComposite numberWaste managementCementMetallurgyEngineering

Abstract

fetched live from OpenAlex

Today, scrap tire is disposed of by open burning which harms the environment. To reduce the wastage of the resource, a sustainable approach is needed to dispose of the waste tire as a partial replacement of the fine aggregate in concrete mixes. This research has been conducted with a focus on the chemical attack of an engineered cementitious composite (ECC) containing a high volume of crumb rubber in terms of durability, behaviour, and comparison with conventional concrete. Two variables have been considered in developing rubberized ECC mixtures, that is, the amount of crumb rubber as a replacement for fine aggregate by volume of 0%–30% and PVA fibres by volume of 0%–2% to cementitious materials. The resistance properties of ECC incorporating crumb rubber were investigated for 13 different variable combinations developed using response surface methodology (RSM). The experimental results revealed that the presence of crumb rubber in the ECC matrix enhanced the resistance of the ECC in both acidic and sulphate environments. It was also revealed that by incorporating 15% crumb rubber, the loss of compressive strength significantly reduced from 38% to 15%.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicInnovative concrete reinforcement materialsFrench-language works237,207