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Record W2095785574 · doi:10.1139/l07-085

Seismic vulnerability assessment of multi-degree-of-freedom systems based on total input energy and momentary input energy responses

2008· article· en· W2095785574 on OpenAlexvenueno aff
Javad Vaseghi Amiri, Gholamreza Ghodrati Amiri, Behnoud Ganjavi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersInternational Institute of Earthquake Engineering and SeismologyIsfahan University of Technology
KeywordsEnergy (signal processing)Range (aeronautics)Structural engineeringSeismic analysisGround motionTotal energyVulnerability (computing)Duration (music)Environmental scienceSeismologyComputer scienceGeologyEngineeringMathematicsStatisticsAcousticsPhysicsDisplacement (psychology)

Abstract

fetched live from OpenAlex

In energy-based seismic evaluation and design, input energy as a seismic demand parameter and hysteretic energy are two important factors. Previous studies for seismic assessment and design based on energy approach have been generally limited to single-degree-of-freedom (SDOF) structures. The purpose of this paper is to find a suitable energy-based parameter for estimation of the damaging potential of ground motion in reinforced concrete frames of multi-degree-of-freedom (MDOF) systems. In this regard, 40 common reinforced concrete frames subjected to four different earthquakes have been analyzed. The results indicate that maximum momentary input energy is a more appropriate parameter than maximum total input energy for estimation of structural damage in short-duration earthquakes or those in which the major damage to structures happens within a short duration of ground motion. In addition, in earthquakes with a wide frequency range, the predominant period of ground motion in Fourier spectrum is shown to be the period corresponding to maximum input energy.

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 score0.092
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.211
Teacher spread0.196 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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