MétaCan
Menu
Back to cohort
Record W2314019286 · doi:10.1061/40616(281)1

The Canadian Highway Bridge Design Code Approach to the Design of Composite Beams and Girders

2002· article· en· W2314019286 on OpenAlexaffabout
Denis Beaulieu, David Kennedy, André Picard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversité LavalUniversity of Alberta
FundersMinistry of Food and Drug Safety
KeywordsGirderStructural engineeringMoment (physics)Flexural strengthComposite numberStress (linguistics)Bridge (graph theory)Building codeYield (engineering)EngineeringMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

In September 2000, a new Highway Bridge Design Code, based on limit states design principles, will be re-established for all of Canada. The highlights of and rationale for the design provisions for composite beams and girders in this code are presented. These provisions have been greatly simplified and rationalized as compared to previous codes. A unified approach is proposed for evaluating the factored moment resistance of Class 1, 2 and 3 steel sections (plastic, compact and non-compact sections, respectively), and stiffened plate girders. Composite beams and girders with steel sections, symmetrical or unsymmetrical with respect to the flexural axis, are treated similarly. An equivalent plastic stress distribution technique, which is achieved by neglecting portions of the web, is used for the evaluation of the factored moment resistance of the most slender sections in positive moment regions. This method was calibrated against a more precise but more cumbersome elasto-plastic stress distribution technique, as well as against existing code recommendations. The factored moment resistance of Class 3 (non-compact) sections and stiffened plate girders in negative moment regions is the only resistance based on a linear stress distribution at first appearance of yield in the steel section.

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: none
Teacher disagreement score0.901
Threshold uncertainty score0.999

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 head0.035
GPT teacher head0.194
Teacher spread0.159 · 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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicStructural Load-Bearing AnalysisFrench-language works237,207