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Record W2947571293 · doi:10.1680/jstbu.18.00220

Prestressed lightweight concrete slabs strengthened with carbon-fibre-reinforced polymer

2019· article· en· W2947571293 on OpenAlexaff
Vahid Azami, Mehdi Dehestani, Hadi Nazarpour

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceComposite materialDeflection (physics)StiffnessPrestressed concreteStructural engineeringPolymer

Abstract

fetched live from OpenAlex

The effect of applying carbon-fibre-reinforced polymer sheets together with a prestress force to strengthen lightweight reinforced concrete slabs was investigated experimentally and numerically. As reported in this paper, load–deflection curves, stiffness, energy-absorption capacity and crack width of the prestressed slabs were examined and a detailed numerical model is presented. Strengthening with polymer layers resulted in a larger lever arm, while prestressing slightly reduced the lever arm of the section. The maximum deflection of prestressed slabs was smaller than that of the control specimen due to a small reduction in energy absorption. Using polymer sheets increased both the strength and maximum ultimate deflection simultaneously due to a higher energy-absorption capacity. The formation and propagation of cracks was postponed due to prestressing, so the ultimate crack width was reduced. Owing to the increase in stiffness, the polymer strengthening reduced crack spacing, crack width and crack growth for all strengthened specimens. In addition, a parametric study revealed that the variations in tendon eccentricity had a significant influence on the force–displacement response of slabs, whereas variations in characteristic strength and dilation angle only had a slight effec/t.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.003
GPT teacher head0.174
Teacher spread0.170 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Structures and BuildingsSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207