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Record W6938872572 · doi:10.60692/ca9g8-g8h74

Experimental and Numerical Assessment of Reinforced Concrete Beams with Disturbed Depth

2023· article· en· W6938872572 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReinforcementBeam (structure)Reinforced concreteParametric statisticsShear (geology)Reinforced solidFinite element methodCracking

Abstract

fetched live from OpenAlex

This paper investigates numerically and experimentally the performance of reinforced concrete (RC) beam with unequal depths subjected to combined bending and shear. Such beams can geometrically be considered for unleveled reinforced concrete (RC) floor slab-beam system. However, it may generate critical disturbances in stress flow at the re-entrant corner (i.e. location of drop in beam depth). This research investigates the use of shear reinforcement and geometric properties to enhance cracking characteristics, yielding, ultimate load-carrying capacity, and exhibiting ductile failure mode. Ten reinforced concrete (RC) beams were constructed and tested experimentally considering the following key parameters: recess length, depth of smaller beam nib, and amount and layout of shear reinforcement at re-entrant corner. Finite element analysis (FEA) with material non-linearity was conducted in two RC beams that were tested experimentally to validate the computer modelling. The FEA models were then extended to conduct a parametric study to investigate the influence of geometric parameters (beam shape and width) and amount and arrangement of shear reinforcement on the structural response. Results confirmed that geometric properties and ratio of shear reinforcement at the re-entrant region significantly affect the behavior of reinforced concrete beam with unequal depths in terms of first cracking, yielding level, ultimate load carrying capacity and mode of failure.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.627

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 communication0.0000.001
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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGreater South Information SystemSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207