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Record W2088836166 · doi:10.1155/2011/428367

Examination of Strength Modeling Reliability of Physical Tests on Structural Concrete Columns

2011· article· en· W2088836166 on OpenAlexafffundabout
Sher Ali Mirza

Bibliographic record

VenueAdvances in Civil Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringBendingFinite element methodCompressive strengthCurvatureMaterials scienceBucklingReliability (semiconductor)Flexural strengthComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

Comparisons and analyses of strengths from selected computational procedures were undertaken of more than 500 physical tests of rectangular, tied, structural concrete columns reported in the published literature. The computational procedures compared with the physical tests and with each other include a commercially available nonlinear finite element modeling software and the Canadian Standards Association (CSA) Standard A23.3-04. The requirements of the American Concrete Institute (ACI) 318-08 are very similar to those of the CSA A23.3-04, and hence, strength comparisons and analyses reported here are also applicable to ACI 318-08. The physical tests used for comparison were conducted on columns that were braced and pinned at both ends and were constructed using normal-density concrete with a compressive strength between approximately 17 and 57 MPa. The columns were subjected to short-term loads producing pure axial force, axial force combined with symmetrical single-curvature bending, or pure bending. Major variables included the concrete strength, the end eccentricity ratio, the slenderness ratio, the longitudinal reinforcing steel index for reinforced concrete or the structural steel index for composite columns, and the transverse reinforcement (tie/hoop) volumetric ratio. The study provides an examination of the reliability of the computational methods examined.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.771

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 communication0.0000.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations5
Published2011
Admission routes3
Has abstractyes

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