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Record W2043583528 · doi:10.2495/safe-v2-n2-184-196

A better rigid-plastic estimate for earthquake-induced plastic displacements

2012· article· en· W2043583528 on OpenAlexvenueno aff
Maria Cristina Porcu, G. Carta

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRange (aeronautics)Ductility (Earth science)PlasticityAccelerationStructural engineeringYield (engineering)Materials sciencePhysicsEngineeringComposite materialClassical mechanics

Abstract

fetched live from OpenAlex

The earthquake ductility demand on structures may be predicted by means of a rigid-plastic method, which derives the maximum plastic response of elastic-plastic oscillators from that of a simpler rigid-plastic model. The maximum response of the latter is a purely plastic one and may be obtained from the earthquake rigid-plastic pseudo-spectrum, as a function of the oscillator yield acceleration. The results of a wide investigation presented in this paper show that such a method generally leads to a conservative and reliable enough estimate of the maximum plastic displacements. Small mean errors are in fact found for both comparatively short-period and long-period oscillators. In the medium-period range, however, the rigid-plastic prediction is found to be less satisfactory. This is due to the appliance in that range of an empirical formula, which estimates the discrepancy between the elastic-plastic and the rigid-plastic peak response. To improve the rigid-plastic prediction in the medium-period range, a new semi-empirical formula is derived in the paper which is shown to halve, on average, the error in estimating the earthquake ductility demand on medium-period oscillators. Thanks to the new formula, the mean relative errors are always kept below 15%, whatever the earthquake and the oscillator. This makes the rigid-plastic method competitive with respect to other approximate methods, as discussed in the paper.

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 score0.094
Threshold uncertainty score0.485

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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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