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Record W2101074694 · doi:10.1139/cjce-2011-0398

The effect of element strength assignment on the torsional response of stiffness-eccentric systems

2013· article· en· W2101074694 on OpenAlexvenueno aff
George K. Georgoussis

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersEuropean Social FundEuropean Commission
KeywordsStructural engineeringStiffnessTorqueEccentricEngineeringTorsion (gastropod)Shear (geology)Base (topology)MathematicsPhysicsMaterials scienceMathematical analysis

Abstract

fetched live from OpenAlex

Building structures of low or medium height are usually designed with a pseudostatic approach using a base shear much lower than that predicted from an elastic spectrum. Given this shear force, the objective of this paper is to evaluate the effect of the element strength assignment (as determined by several building codes) on the torsional response of inelastic single-storey eccentric structures and to provide guidelines for minimizing this structural behaviour. It is demonstrated that the expected torque about the centre of mass (CM) may be, with equal probability, positive (counterclockwise) or negative (clockwise). This result means that the torsional strength should also be provided in equal terms in both rotational directions, and therefore the base shear and torque (BST) surface of a given system must be symmetrical (or approximately symmetrical). In stiffness-eccentric systems, appropriate BST surfaces may be obtained when a structural design is based on a pair of design eccentricities in a symmetrical order about CM, and this is shown in representative single-storey building models under characteristic ground motions.

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)0.007
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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