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Record W4390937751 · doi:10.1139/cjce-2023-0122

Experimental and numerical performance analysis of alccofine-based crumb rubber concrete under quasi-static cyclic load

2024· article· en· W4390937751 on OpenAlexvenueno aff
Puja Haldar, Somnath Karmakar

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringCrumb rubberDissipationDemolitionDuctility (Earth science)Dynamic loadingMaterials scienceFinite element methodDynamic load testingSeismic loadingNatural rubberEngineeringComposite materialCreepCivil engineering

Abstract

fetched live from OpenAlex

Structure, during its design period, may face unpredictable dynamic loading: wind, earthquake, and blast. Unavoidably, loading rates impact the dynamic behaviours of structures. Besides, the infrastructure system is more susceptible to dynamic loading. Therefore, a study on the dynamic behaviour of concrete under quasi-static cyclic load is inevitable. Fortunately, the researchers explored that innovative materials could respond better to extreme dynamic loading. One of the serious issues is the demolition of rubber tyres, which may be solved by converting the same into crumb form as an innovative construction material. In the present study, experimental and finite element method-based numerical analysis shows a substantial improvement in fatigue, flexural, ductility, inelastic performance, and energy dissipation capacity of the alccofine-based crumb rubber concrete beam. These performances are observed experimentally under monotonic and quasi-static cyclic loads. The load-carrying capacity has also been predicted analytically; the results agree with the experimental and numerical ones.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.216
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.

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
Published2024
Admission routes1
Has abstractyes

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