MétaCan
Menu
Back to cohort
Record W2088508874 · doi:10.1139/l00-077

Second-order analysis for semirigid steel frame design

2001· article· en· W2088508874 on OpenAlexfundvenueno aff
Lei Xu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRigidity (electromagnetism)Structural engineeringColumn (typography)Frame (networking)Beam (structure)Connection (principal bundle)Steel frameNonlinear systemComputer scienceEngineeringMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents an analysis method in which both the nonlinear rotational behaviour of beam-to-column connections and second-order (P-Δ and P-δ) effects of beam-column members are explicitly taken into account. By adopting the concept of an end-fixity factor, the proposed method is comprehensive regardless of the end rotational conditions of beam-column members and applies to frames with any combination of pinned, semirigid and rigid beam-to-column connections. In addition, the end-fixity factor simplifies the analysis procedure for semirigid frames. More importantly, the end-fixity factor may be valuable to the design of such structures because it provides a physical interpretation of the extent of rigidity available in a connection. Thus, it provides a convenient way for designers to compare the structural responses of a semirigid member to that of a rigid one. Examples are presented to demonstrate the validity and efficiency of the proposed approach. With only minor modifications, the proposed method is readily implemented in existing rigid frame analysis and design computer programs.Key words: steel frame, semirigid connection, second-order analysis, P-Δ effect.

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.194
Teacher spread0.181 · 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

Citations16
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicStructural Load-Bearing AnalysisFrench-language works237,207