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Record W2941382995 · doi:10.2749/vancouver.2017.1734

Opening effect on punching shear strength of RC slabs

2017· article· en· W2941382995 on OpenAlexaff
Georgios P. Balomenos, Aikaterini S. Genikomsou, Marianna Polak

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of WaterlooQueen's University
Fundersnot available
KeywordsSlabStructural engineeringPunchingShear (geology)Finite element methodReinforced concreteMaterials scienceShear strength (soil)Geotechnical engineeringGeologyComposite materialEngineering

Abstract

fetched live from OpenAlex

Punching shear failure can happen in reinforced concrete flat slabs due to the development of high shear stresses in the slab-column connection area. These shear stresses are increased when openings are created, since the presence of openings reduce the concrete area that sustains the shear stresses. In this paper, finite element analysis (FEA) with the damaged plasticity model for concrete in ABAQUS is performed to simulate the opening effect in reinforced concrete slabs without shear reinforcement. A previously tested and analyzed interior slab-column connections is considered. The effect of the location and the size of the opening on the punching shear resistance are investigated. The punching shear capacity of the analyzed specimens is calculated using the equations of two current design provisions for punching shear (ACI 318-14, Eurocode2-2004) and compared with the numerical results. A probabilistic analysis using Monte Carlo simulation for both design codes is considered. Finally, fragility analysis is performed in order to estimate the probability of the estimated punching shear resistance related with the opening size and distance.‌‌

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.270
Teacher spread0.256 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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