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Record W2117244929 · doi:10.1142/s1793431112500042

SEISMIC LATERAL FORCE DISTRIBUTION FOR DUCTILITY-BASED DESIGN OF STEEL PLATE SHEAR WALLS

2012· article· en· W2117244929 on OpenAlexfundno aff
Swapnil B. Kharmale, Siddhartha Ghosh

Bibliographic record

VenueJournal of Earthquake and Tsunami · 2012
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaMcMaster University
KeywordsShear wallStructural engineeringDuctility (Earth science)RoofShear (geology)Seismic analysisSteel plate shear wallShear forceStructural loadDisplacement (psychology)Restoring forceGeotechnical engineeringMaterials scienceGeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

The thin unstiffened steel plate shear wall (SPSW) system has now emerged as a promising lateral load resisting system. Considering performance-based design requirements, a ductility-based design was recently proposed for SPSW systems. It was felt that a detailed and closer look into the aspect of seismic lateral force distribution was necessary in this method. An investigation toward finding a suitable lateral force distribution for ductility-based design of SPSW is presented in this paper. The investigation is based on trial designs for a variety of scenarios where five common lateral force distributions are considered. The effectiveness of an assumed trial distribution is measured primarily on the basis of how closely the design achieves the target ductility ratio, which is measured in terms of the roof displacement. All trial distributions are found to be almost equally effective. Therefore, the use of any commonly adopted lateral force distribution is recommended for plastic design of SPSW systems.

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: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.338

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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