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Record W2313027120 · doi:10.1061/9780784479742.022

Verification of Proposed Seismic Response Factors and Performance Assessment with the Economics of Code Designed High-Rise Steel Buildings

2016· article· en· W2313027120 on OpenAlexaff
Nadeem Hussain, M. Shahria Alam

Bibliographic record

VenueGeotechnical and Structural Engineering Congress 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic and Structural Analysis of Tall Buildings
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSeismic analysisRange (aeronautics)Margin (machine learning)Code (set theory)Incremental Dynamic AnalysisComputer scienceEngineeringCivil engineeringStructural engineering

Abstract

fetched live from OpenAlex

Seismic response factors do not offer uniform margin of safety and economy of buildings for different seismic regions. To verify these factors, four high-rise regular steel buildings, ranging from 8 to 20 stories are selected and designed according to current design codes. These factors are estimated using a number of inelastic pushover analyses (IPAs) and incremental dynamic analyses (IDAs), and their respective safety margins are assessed. The results indicated a possibility of increasing design response factors. The selected buildings are redesigned and their performances are assessed and verified using the proposed increase in seismic design response factors. Cost comparison between the code designed buildings and the proposed seismic design factor buildings is made. The proposed study provides comparisons of seismic response factors between the code-designed buildings and alternative proposed design for the range of the selected buildings along with their economic benefits. The recommendations of this study may provide practical insights to the designers and stake holders to achieve more cost-effective and optimistic designs on a wide range of high-rise steel 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.442

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 head0.187
Teacher spread0.182 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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