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Record W2094784482 · doi:10.1139/l07-077

Assessment of ductility demand and reliability of bilinear single-degree-of-freedom systems under earthquake loading

2007· article· en· W2094784482 on OpenAlexafffundvenue
Han Hong

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDuctility (Earth science)Structural engineeringStiffnessBilinear interpolationProbabilistic logicReliability (semiconductor)MathematicsEngineeringMaterials scienceStatisticsCreep

Abstract

fetched live from OpenAlex

Probabilistic assessments of the ductility demand and reliability analysis were carried out for hysteretic bilinear single-degree-of-freedom (SDOF) systems. The assessment considered a set of strong ground motion records, and focused on evaluating the mean and coefficient of variation of the ductility demand for a given normalized yield strength. Results indicated that the ductility demand could be modeled as a Frechet (extreme value type II) variate. Based on the obtained results, empirical equations were provided to predict the mean ductility demand for hysteretic bilinear SDOF systems considering different natural vibration periods, damping ratios, and post-yield stiffness to initial stiffness ratios. Numerical results showed that the coefficient of variation of the ductility demand can go as high as one, depending on the characteristics of the structure. Also, a simple approach was given to estimate the probabilities of incipient damage and incipient collapse using the developed probabilistic characterization of the ductility demand. This approach, which could be suitable for design code calibration analysis, is illustrated numerically in this study.

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)0.000
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 score0.229
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations36
Published2007
Admission routes3
Has abstractyes

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