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Torsional Responses under Bidirectional Seismic Excitations: Effect of Instantaneous Load Eccentricities

2012· article· en· W2118268895 on OpenAlexaff
Han Hong

Bibliographic record

VenueJournal of Structural Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsDisplacement (psychology)Nonlinear systemStiffnessCenter of mass (relativistic)Structural systemStructural engineeringGround motionPhysicsLinear elasticityMechanicsEngineeringFinite element method

Abstract

fetched live from OpenAlex

Torsional response for one-way or two-way asymmetric linear/nonlinear structural systems under uni- and bidirectional seismic excitations increases the maximum displacement demand on structural systems. In this study, we consider instantaneous load eccentricities caused by the motion of the center of mass. The eccentricities, which are time dependent, exist even for linear elastic two-way symmetric structures under seismic excitations, as the relative displacement between the center of mass and center of stiffness may not necessarily be negligible. The quantification of this second-order effect on structural response is not available in the literature. Equations are developed and some numerical results are obtained by incorporating this second-order effect for linear elastic structures under bidirectional excitations to quantify the importance of this phenomenon. The results obtained by considering more than 100 ground motion records suggest that, on average, an underestimation of seismic displacement demand can occur if this second-order effect is ignored, especially for two-way symmetrical systems. The degree of underestimation depends on the dynamic characteristics of the system and the record.

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)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.006
GPT teacher head0.216
Teacher spread0.211 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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