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Record W2184425576 · doi:10.3850/978-981-07-8859-9_40

Evaluation of Current Strut-and-Tie Design Provisions for Deep Beams by a Concrete Limit Analysis

2014· article· en· W2184425576 on OpenAlexaboutno aff
Jinwoo Kim, Sung‐Gul Hong, Young Hak Lee, Hee-Cheul Kim, Dae Jin Kim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringShear (geology)Current (fluid)Reinforced concreteShear strength (soil)Building codeSpan (engineering)Limit (mathematics)EngineeringGeologyMaterials scienceMathematicsElectrical engineeringMathematical analysisComposite material

Abstract

fetched live from OpenAlex

The current American Concrete Institute (ACI), Canadian Standard Associate (CSA) and CEB-FIP code provisions suggest that deep beams should be designed using the strut-and-tie model approach. This paper investigates the validity of the current ACI, CSA and CEB-FIP code provisions on the shear strength of simply supported reinforced concrete deep beams by comparing them with the shear strength equations proposed by Hong et al. (1) The comparison shows that the current ACI and CEB-FIP design codes can provide reasonable shear strength estimates that approximately correspond to the 70 to 80 % of the predictions by the shear strength equations. In contrast, the current CSA code relatively underestimates the shear strength for cases with large shear-span ratio.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.293
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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