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Record W2008495010 · doi:10.1139/l02-029

Design for forces induced by seismic torsion

2003· article· en· W2008495010 on OpenAlexfundvenueaboutno aff
Jagmohan Humar, Soheil Yavari, Murat Saatçioğlu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTorsion (gastropod)Structural engineeringBuilding codeStiffnessSeismic analysisMoment of inertiaRigidity (electromagnetism)InertiaComputer scienceEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Eccentricities between the centres of rigidity and centres of mass in a building cause torsional motion during an earthquake. Seismic torsion leads to increased displacement at the extremes of the building and may cause distress in the lateral load-resisting elements located at the edges, particularly in buildings that are torsionally flexible. For an equivalent static load method of design against torsion, the 1995 National Building Code of Canada specifies values of the eccentricity of points through which the inertia forces of an earthquake should be applied. In general, the code requirements are quite conservative. They do not place any restriction on the torsional flexibility, however. New proposals for 2005 edition of the code which simplify the design eccentricity expressions and remove some of the unnecessary conservatism are described. The new proposals will require that a dynamic analysis method of design be used when the torsional flexibility of the building is large. Results of analytical studies, which show that the new proposals would lead to satisfactory design, are presented.Key words: torsional response to earthquake, natural torsion, accidental torsion, design for torsion, National Building Code of Canada, interdependence of strength and stiffness.

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 score0.756
Threshold uncertainty score0.535

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 head0.011
GPT teacher head0.183
Teacher spread0.172 · 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

Citations25
Published2003
Admission routes3
Has abstractyes

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