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Record W2115356225 · doi:10.1061/41084(364)69

A Simplified Axial-Shear-Flexure Interaction Approach for Load and Displacement Capacity of Reinforced Concrete Columns

2009· article· en· W2115356225 on OpenAlexaff
Hossein Mostafaei, Frank J. Vecchio, Toshikazu Kabeyasawa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsNational Research Council CanadaUniversity of Toronto
Fundersnot available
KeywordsStructural engineeringShear (geology)Reinforced concreteMaterials scienceSimple shearEngineeringComposite material

Abstract

fetched live from OpenAlex

A simple performance-based evaluation approach is presented based on interactions of axial, shear, and flexure mechanisms to estimate the axial and lateral deformation and load capacities of reinforced concrete columns. The developed model 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 response of reinforced concrete columns subjected to axial, flexure and shear force. The analytical model was developed by coupling an axial-shear model, which is a biaxial shear model, and an axial-flexure model, which is the traditional section analysis. Axial deformation interaction is the main compatibility condition taken into account in this approach. Equilibrium conditions are satisfied through a simple shear and flexure stress relation. A series of reinforced concrete columns subjected to axial and lateral loads, tested by various researchers, were evaluated by the simple developed approach and the results were compared with the test data; consistent correlation and agreement were achieved. This paper describes the formulation, implementation and verification of the modified approach. A future attempt is to modify the ASFI method for response estimation of reinforced concrete columns in fire under axial load and lateral deformation induced by thermal expansion.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.665

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 head0.015
GPT teacher head0.235
Teacher spread0.219 · 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 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

Citations4
Published2009
Admission routes1
Has abstractyes

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