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Record W6980181346

BEHAVIOUR AND DESIGN OF STRENGTHENED BUILT-UP HYBRID STEEL COLUMNS

2006· article· en· W6980181346 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsParametric statisticsBracingFlangeCompression (physics)ResidualResidual strengthTrussColumn (typography)Buckling
DOInot available

Abstract

fetched live from OpenAlex

Steel bridges designed before the mid-1950s may be deficient according to current design code requirements and thereby require strengthening. This thesis explores the responses of W-shaped compression members, such as existing columns, bents, bracing or truss members, reinforced at the flanges by new steel cover plates. The primary research objectives are: (1) to model the behaviour, accounting for residual and locked-in dead load stresses, different yield strengths of the original W-shape and new flange cover plates, initial out-of-straightness and end eccentricity; and (2) to develop a rationally based practical design method for the compressive resistance of a built-up hybrid compression member. The research reported in this thesis first develops the mechanics for the resistance of built-up hybrid steel compression members from principles of equilibrium, compatibility, and force-deformation relationships. Based on these mechanics, the Refined Numerical Analysis Model is developed and implemented in a computer program to approximate column capacities and validated by comparison to column design curves in the Canadian Highway Bridge Design Code. A parametric study is presented to determine the sensitivity of compressive resistance to the magnitude of residual stresses, locked-in dead load stresses, yield strength of the W-shape and end eccentricity. An optimal simplified design method for practical design usage is identified from four candidate procedures.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

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 head0.050
GPT teacher head0.259
Teacher spread0.209 · 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.

Study designObservational
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

Citations1
Published2006
Admission routes1
Has abstractyes

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