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Record W2006092043 · doi:10.1680/macr.10.00054

Shear strength prediction for reinforced concrete beams without stirrups

2011· article· en· W2006092043 on OpenAlexaboutno aff
Wei-wei Wei, Yi Che, Jinxin Gong

Bibliographic record

VenueMagazine of Concrete Research · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsShear (geology)Structural engineeringReinforced concreteTest dataShear strength (soil)Geotechnical engineeringShear stressMaterials scienceGeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

Traditionally, shear design of concrete beams without stirrups has relied on empirical equations derived from laboratory experiment data. However, such methods lack a sound theoretical basis for shear failure of reinforced concrete (RC) structures due to the complexity of the failure mechanism. In the 1980s, a relatively rational model based on the modified compression field theory (MCFT) was developed at the University of Toronto and subsequently accepted worldwide; it forms the basis of shear provisions in Canadian standards and American specifications for the design of concrete structures. This paper describes further research based on the MCFT. An expression for the average shear stress across a crack was derived and a simplified equation of shear strength considering the size effect in shear was developed. The obtained equations were verified with extensive sets of experimental data from different sources (598 beams in total). It was found that the variation coefficients of the ratio of shear strength calculated using the derived average shear stress across a crack based on the MCFT and the simplified expressions to the test data was small; the mean was about 0·8. It is thus considered that the simplified expressions are suitable for shear analysis and design of RC beams without stirrups.

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)0.001
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 score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.307
Teacher spread0.245 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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