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Record W2142821943 · doi:10.1139/l09-126

Performance of Type D and Type LD steel plate walls

2010· article· en· W2142821943 on OpenAlexafffundvenueabout
Anjan K. Bhowmick, Gilbert Y. Grondin, Robert G. Driver

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaUniversity of Alberta
KeywordsStructural engineeringDuctility (Earth science)Type (biology)Finite element methodSeismic analysisQuasistatic processBeam (structure)Moment (physics)EngineeringGeotechnical engineeringMaterials scienceGeologyComposite materialPhysics

Abstract

fetched live from OpenAlex

A finite element model is developed to study the behaviour of unstiffened steel plate walls. The model includes both material and geometric nonlinearities and strain rate effects. The model is first validated using the results from quasistatic and dynamic experimental programs. The validated finite element model is then used to study the performance of four storey and eight storey steel plate walls with moment-resisting beam-to-column connections under spectrum compatible seismic records for Vancouver and Montreal. Two different steel plate wall types defined in the current Canadian standard CAN/CSA-S16–01 are considered, namely, Type D (ductile) and Type LD (limited-ductility) plate walls. All the Type D walls, designed according to the capacity design provisions, exhibit better inelastic seismic responses than the Type LD plate walls. The analyses of eight storey steel plate walls show that in high seismic regions, such as Vancouver, medium- to high-rise Type LD plate walls may exhibit yielding in columns in intermediate floors. The study also shows that in more moderate seismic regions, like Montreal, Type LD plate walls behave in a stable and ductile manner and can be used for low- to medium-rise buildings.

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 score0.098
Threshold uncertainty score0.393

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 head0.005
GPT teacher head0.167
Teacher spread0.162 · 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

Citations9
Published2010
Admission routes4
Has abstractyes

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