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Record W4398351107 · doi:10.1177/87552930241249704

Ground‐motion models for inelastic response spectra using NGA‐West2 database

2024· article· en· W4398351107 on OpenAlexaff
Mahdi Bahrampouri, Yousef Bozorgnia, Kenneth W. Campbell, Silvia Mazzoni

Bibliographic record

VenueEarthquake Spectra · 2024
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsCampbell Scientific (Canada)
FundersCalifornia Department of TransportationU.S. Department of Transportation
KeywordsGround motionSpectral lineMotion (physics)GeologyDatabaseSeismologyPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This article presents ground‐motion models (GMMs) for inelastic response spectra using the NGA‐West2 database. The inelastic response spectra are defined in terms of constant ductility. The GMMs are used to observe the effect of event scenarios, site conditions, and oscillator properties on maximum displacement. Using a large database enabled us to quantify the magnitude scaling of inelastic response as compared to that for the traditional elastic GMMs. We observe that using the magnitude scaling of elastic GMM for hazard analysis of inelastic structural response can be unconservative for large magnitudes and large ductility ratios. To compare our models with models in the literature, we use the developed GMMs to compute the Reduction Factor due to inelasticity. Comparison of this model with models in the literature shows the importance of considering the effect of magnitude on the Reduction Factor. We also observe that the ratio of RotD100/RotD50 is larger for inelastic single‐degree‐of‐freedom system than that for elastic system, and the ratio increases with increasing 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)0.000
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 score0.406
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.252
Teacher spread0.225 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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