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Uniaxial Shear-Flexure Model for Reinforced Concrete Elements

2008· article· en· W2083985600 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
Fundersnot available
KeywordsShear (geology)Structural engineeringReinforced concreteComputationMaterials scienceDeformation (meteorology)Simple shearGeologyGeotechnical engineeringComputer scienceEngineeringComposite materialAlgorithm

Abstract

fetched live from OpenAlex

A simple approach was developed for performance-based analysis of reinforced concrete columns subjected to shear, flexure and axial loads. This method is based on a simplification of a consistent but relatively more complex approach known as the axial-shear-flexure interaction (ASFI) method, which is able to predict the full load-deformation relationships of reinforced concrete columns subjected to axial, flexure and shear force. The uniaxial shear-flexure model (USFM), presented herein, can also predict comparable full load-deformation responses. In it, however, the computation-intensive iteration process for shear modeling used within the ASFI approach is eliminated and the formulation is simplified. This paper describes the formulation, implementation and verification of the USFM approach.

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 score0.072
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 communication0.0000.001
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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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