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Record W3046742091 · doi:10.1139/cjce-2019-0626

Simplified material model for concrete containing high-content of tire-derived coarse aggregate under compression loading

2020· article· en· W3046742091 on OpenAlexaffvenue
Abdelmoneim El Naggar, Hany El Naggar, Pedram Sadeghian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsToughnessMaterials scienceAggregate (composite)Compressive strengthNatural rubberComposite materialProperties of concreteCompatibility (geochemistry)Compression (physics)Crumb rubberElastic modulusStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The flexible properties of the shredded rubber tires give rubberized concrete desirable properties such as lower relative density, and better dampening ability, higher toughness, and improved deformability resulting in an enhanced dynamic performance. Limited work has been done in modeling these effects. In this study, natural coarse aggregates in concrete mixes were replaced by volume by shredded tires up to 100% replacement ratios following 10% increments. Compression tests were conducted to investigate the effects of shredded tires on the mechanical properties of concrete. As expected, the results showed a decrease in the compressive strength and the elastic modulus of the concrete as the replacement ratio increase. Due to compatibility, strain at peak values increased and the integrity of the concrete after failure was enhanced. Models describing the effects that shredded tires have on the mechanical properties of the concrete were developed and validated against experimental data from various researchers.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.043
GPT teacher head0.215
Teacher spread0.172 · 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 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

Citations9
Published2020
Admission routes2
Has abstractyes

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