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Finite-Element Analysis of Reinforced Concrete Slabs with Punching Shear Reinforcement

2016· article· en· W2463342275 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 · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReinforcementSlabStructural engineeringFinite element methodShear (geology)Parametric statisticsPunchingReinforced concreteMaterials scienceGeologyComposite materialEngineeringMathematics

Abstract

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Finite-element analyses (FEA) of reinforced concrete slab-column connections with shear reinforcement are presented and discussed. Ultimate loads and crack patterns are shown and compared to the experimental findings. The damaged plasticity model in ABAQUS, previously calibrated and validated on tested slabs without shear reinforcement, is adopted to properly simulate the behavior of slab-column connections with shear reinforcement. Four interior slab-column specimens were tested under vertical loading applied through the column. One slab specimen was without shear reinforcement, while the other three differed in the amount of the shear bolts. The objective of this paper is to propose a novel numerical modeling strategy for simulating and analyzing shear reinforced slabs with shear bolts. For that purpose, in the numerical analyses, four different approaches for modeling the shear bolt are introduced and described. Discussion and comparison on the predicted failure loads from the design codes and models in conjunction with the experimental and numerical data, are presented. Finally, a parametric numerical study is conducted, where the rectangular and the radial shear reinforcement arrangements are compared.

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 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 score0.121
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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