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Serviceability and Strength of Concrete Floors and Bridge Decks: Grid Analogy

2019· article· en· W2938944667 on OpenAlexaff
Amin Ghali, Ramez B. Gayed

Bibliographic record

VenueJournal of Structural Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStructural engineeringCartesian coordinate systemTorsion (gastropod)Deflection (physics)Neutral axisBending momentPrincipal axis theoremServiceability (structure)Internal forcesGridMidpointCentroidPrestressed concreteGeometryEngineeringMathematicsPhysicsClassical mechanicsBeam (structure)

Abstract

fetched live from OpenAlex

This paper analyzes bridge decks and two-way horizontal slabs with or without beams or drop panels using a grid analogy. The grid members are beams with a horizontal centroidal principal axis. The nodal displacements are a downward deflection, two rotations about Cartesian axes, and two translations along the Cartesian axes. The internal forces at a cross section of a grid member were a vertical shearing force, a twisting moment, a bending moment about horizontal principal centroidal axis, and a normal force at centroid. The model gives deflections in agreement with the solution of the differential equation of elastic deflection of thin plates. For ultimate strength design of sections and deflection calculation, it is recommended to use analysis of a grid with zero twisting moments. This can be done by setting a negligible value for the torsion constant for all members. Long-term curvatures and deflections are predicted using equations derived for beams.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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