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Record W2364891725

Influences of pinching and P-.TRIAGL. effects on seismic ductility demand and damage index

2011· article· en· W2364891725 on OpenAlexaff
Bo Yu, Taojun Liu, Hong Hanping

Bibliographic record

VenueDizhen gongcheng yu gongcheng zhendong · 2011
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsDuctility (Earth science)StiffnessStructural engineeringLog-normal distributionIndex (typography)GeologyMaterials scienceMathematicsEngineeringStatisticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Assessment of the influences of pinching and P-Δ effects on seismic ductility demand and Park-Ang seismic damage index of inelastic single-degree-of-freedom(SDOF) system is carried out.The hysteretic behaviour of the SDOF system is described using the Bouc-Wen model taking into account strength and stiffness degradations as well as pinching and P-Δ effects.Analysis results show that the P-Δ and the pinching effects influence the seismic ductility demand,while the effect induced by the vertical excitation is negligible.Probabilistic models for predicting the mean and coefficient of variation of seismic ductility demand considering the pinching and P-Δ effects are recommended based on the samples obtained from 69 California records.It is suggested that for short-period system,the seismic ductility demand can be modeled as either the Lognormal or the Frechet distribution,while for long-period system,the Frechet distribution is preferred.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2011
Admission routes1
Has abstractyes

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