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Record W2320304442 · doi:10.1061/40616(281)74

Design and Application of Partially Encased Non-Compact Composite Columns for High-Rise Buildings

2002· article· en· W2320304442 on OpenAlexaffabout
Richard B. Vincent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsMicromolding Solutions (Canada)Canam Group (Canada)
Fundersnot available
KeywordsColumn (typography)Structural engineeringComposite numberPrefabricationScope (computer science)Work (physics)High riseEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

A new composite column designed specifically to support large gravity loads in high-rise steel buildings is described. The column takes advantage of the in-plant prefabrication of steel, the economical compressive load carrying capacity of concrete, the speed of erection for a steel structure and the fire resistance inherent in a concrete column. The column is specifically designed to simplify the installation and removal of the form work needed to pour the concrete and requires less crane capacity to erect the column due to a much lighter steel section. The extensive test program carried out at three North American universities is summarised. The scope of application for the new column and the design equations that have been proposed are discussed. Finally, an example of the savings that can be achieved by using this column system is demonstrated by comparing the system to the actual design of an existing building. The new composite column has been patented in Canada, the United States of America and has received an International patent.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Citations10
Published2002
Admission routes2
Has abstractyes

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