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Record W1998623531 · doi:10.1139/l06-085

Evaluation of the effects of spectrum-compatible seismic excitations on the response of medium-height reinforced concrete frame buildings

2006· article· en· W1998623531 on OpenAlexvenueaboutno aff
N. Naumoski, Murat Saatçioğlu, Lan Lin, Kambiz Amiri-Hormozaki

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsResponse spectrumStructural engineeringSeismic analysisFrame (networking)Spectrum (functional analysis)EngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Spectrum-compatible seismic excitations are required when dynamic time-history analysis is used for determining the response of a structure. This paper presents results from a study on the effects of different types of spectrum-compatible excitations on the response of medium-height reinforced concrete frame buildings. Two six-storey buildings designed for Vancouver and a five-storey building designed for Montréal were used in the study. Nonlinear time-history analyses were conducted by subjecting the buildings to selected ensembles of spectrum-compatible excitations (i.e., accelerograms). The ensembles used in the study included spectrum-compatible artificial accelerograms, simulated stochastic accelerograms, and recorded earthquake accelerograms (i.e., real accelerograms) scaled to the design spectrum ordinate at the fundamental building period and to the area under the design spectrum within the predominant period range of the building. The responses of the buildings resulting from spectrum-compatible artificial accelerograms and those from scaled real accelerograms were found to be quite similar. Based on the results of this study, the scaling of real accelerograms to spectral area is preferred for obtaining spectrum-compatible accelerograms.Key words: seismic, excitation, response, spectrum, accelerogram, building, drift, curvature, ductility.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.187
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations7
Published2006
Admission routes2
Has abstractyes

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