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Record W1625061605 · doi:10.1139/l2012-103

Selection of seismic excitations for nonlinear analysis of reinforced concrete frame buildings

2013· article· en· W1625061605 on OpenAlexaffvenueabout
Lan Lin, N. Naumoski, Murat Saatçioğlu, Simon Foo, Edmund Booth, Yuling Gao

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of OttawaPublic Works and Government Services CanadaConcordia University
Fundersnot available
KeywordsStructural engineeringSeismic analysisFrame (networking)Reinforced concreteAccelerationLow-riseNonlinear systemSelection (genetic algorithm)Beam (structure)EngineeringGeologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

The selection of seismic motions is one of the most important issues for the time-history analysis of buildings. This paper discusses four different methods for obtaining spectrum-compatible acceleration time histories (i.e., accelerograms) of seismic motions. Based on these methods, four sets of accelerograms compatible with the design spectrum for Vancouver were selected for this study. These included (i) scaled real accelerograms, (ii) modified real accelerograms, (iii) simulated accelerograms, and (iv) artificial accelerograms. The selected sets were used as excitation motions in the nonlinear analysis of three reinforced concrete frame buildings designed for Vancouver. The buildings included a 4-storey, a 10-storey, and a 16-storey building, which can be considered representative of low-rise, medium-rise, and high-rise buildings, respectively. The storey shears, interstorey drifts, and curvature ductilities for beams and columns obtained from the analysis were used for the evaluation of the effects of the selected sets on the responses of the buildings. Based on the results from the analysis, scaled real accelerograms are recommended for use in time-history analysis of reinforced concrete frame 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 score0.186
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.189
Teacher spread0.184 · 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

Citations12
Published2013
Admission routes3
Has abstractyes

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