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Record W3129761947 · doi:10.1139/cjce-2020-0323

A simplified seismic design method for limited-ductility steel multi-tiered concentrically braced frames in moderate seismic regions

2021· article· en· W3129761947 on OpenAlexaffvenueabout
Eshagh Derakhshan Houreh, Ali Imanpour

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBracingStructural engineeringSeismic analysisBraceDuctility (Earth science)Braced frameRoofSeismic loadingEngineeringComputer scienceGeologyFrame (networking)Materials scienceCreepMechanical engineering

Abstract

fetched live from OpenAlex

Steel multi-tiered concentrically braced frames (MT-CBFs) represent a bracing configuration where two or more concentric bracing panels are stacked between the ground and roof levels in tall single-storey buildings. A large proportion of MT-CBFs in Canada are located in low-to-moderate seismic regions (seismic category 0–3) where limited ductility CBFs are often preferred in design. Nevertheless, brace tensile yielding may not occur in all tiers of such frames. Additionally, the analysis and design procedure adopted by the 2019 Canadian steel design standard (CSA S16-19) can become tedious in tall frames with multiple panels. In this paper, the seismic behaviour of limited ductility MT-CBFs in moderate seismic regions of Canada is examined to propose a simplified design method. A set of 16 prototype MT-CBFs is designed in accordance with CSA S16-19, excluding the design requirements prescribed for MT-CBF columns. Nonlinear dynamic analyses are then performed to examine their seismic response. Finally, an efficient design method is proposed in the framework of CSA S16-19.

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 categoriesMeta-epidemiology (narrow)
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 score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.241
Teacher spread0.214 · 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.

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

Citations5
Published2021
Admission routes3
Has abstractyes

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