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Modeling of Shear-Critical Reinforced Concrete Structures Repaired with Fiber-Reinforced Polymer Composites

2008· article· en· W1994272036 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Structural Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Toronto
FundersSungkyunkwan University
KeywordsComposite materialMaterials scienceReinforced concreteShear (geology)Structural engineeringEngineering

Abstract

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This paper describes a study in which finite-element (FE) analysis procedures were used to predict the behavior of a reinforced concrete (RC) frame shear strengthened with fiber-reinforced polymer (FRP) composites. Details are presented of the numerical techniques used to represent the RC frame, the FRP, and the bond properties between the FRP and the concrete. The FE analysis is performed using a two-dimensional nonlinear FE analysis program based on the disturbed stress field model. To augment verification studies undertaken with beam specimens previously tested, a large-scale RC frame with one-span and two-story height was constructed and tested under lateral load conditions. The frame was first heavily damaged in shear, repaired with FRP wrap, and then subjected to a regime of reversed cyclic loads. A detailed comparison is carried out between analytical and experimental results for the hysteretic response, damage mode, crack pattern, and deformation of the frame. It is concluded that reasonably accurate simulations of the behavior of FRP-repaired shear-critical structures can be achieved through finite-element modeling.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.215
Teacher spread0.205 · 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