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Record W4251733475 · doi:10.32920/ryerson.14657514

Axial Strength Of Lightweight Self-Consolidating Concrete Columns With Engineered Cementitious Composite

2021· preprint· en· W4251733475 on OpenAlexaffabout
Sandeep Parajuli

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceDuctility (Earth science)Composite materialSelf-consolidating concreteComposite numberStiffnessDeformation (meteorology)Structural engineeringCore (optical fiber)BendingFinite element methodReinforcementCompressive strengthEngineering

Abstract

fetched live from OpenAlex

Axial load behavior of confined columns with engineered cementitious composite (ECC) wrapping was investigated through experimental, analytical and finite element (FE) investigations. The variables in the study were: geometry (cylindrical and rectangular), presence or absence of longitudinal and tie reinforcement, ECC wrap thickness, types of concrete core (lightweight and normal weight self-consolidating concrete) and type of loading (applied through both core and wrap or core only). The effect of these variables on axial load-deformation response, strain characteristics, failure modes, ductility, energy absorption capacity and axial strength were evaluated. The confined concrete strengths predicted from existing analytical and developed FE models were found to be in good agreement with those of experiments. The axial load capacity and ductility were increased for columns with highest ECC wrap thickness (50 mm) while thinner wrap increased stiffness instead of ductility. Canadian code conservatively predicted axial strength of columns having increased thickness of ECC wrap.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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