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Record W2061217940 · doi:10.1139/l05-072

Comparative strength analyses of structural concrete columns

2006· article· en· W2061217940 on OpenAlexfundvenueno aff
Sher Ali Mirza

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringEurocodeCompressive strengthComposite numberReinforcementReinforced concreteBucklingMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

A comparative study was undertaken of selected computational methods for predicting the strength determined from more than 500 physical tests of rectangular, tied structural concrete columns available in the literature. The study included 354 reinforced concrete and 150 composite steel–concrete columns. The computational methods compared were those of CSA A23.3-94 and Eurocode 2 for reinforced concrete and those of CSA A23.3-94 and Eurocode 4 for composite columns. The physical tests used for comparison were conducted on columns that were braced, pinned at both ends, subjected to short-term loads, and constructed using normal-density concrete with a compressive strength between approximately 17 and 57 MPa. 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 provided insights into the reliability of the computational methods examined. Recommendations for improving the CSA A23.3-94 procedure for the design of reinforced concrete and composite steel–concrete columns are also presented.Key words: columns, composite construction, computations, physical tests, reinforced concrete, reliability, strength.

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 score0.414
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.230
Teacher spread0.215 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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