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Record W2152231178 · doi:10.1139/l06-084

A comparison of two regional seismic damage estimation methodologies

2006· article· en· W2152231178 on OpenAlexfundvenueaboutno aff
Tuna Onur, Carlos E. Ventura, W. D. Liam Finn

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFederal Emergency Management Agency
KeywordsMercalli intensity scaleSpectral accelerationStructural engineeringMasonryPeak ground accelerationIntensity (physics)AccelerationDisplacement (psychology)Response spectrumEnvironmental scienceGeologyGround motionEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a comparison of the two main regional damage estimation methodologies currently in use, namely the modified Mercalli intensity (MMI) based approach and the spectral parameter based approach. In the first methodology, expected damage is related to ground shaking intensity in terms of MMI through damage probability matrices. In the second methodology, the ground motion intensity is described in terms of spectral acceleration (SA), and building response in terms of spectral displacement (SD). Both methodologies were applied to buildings in Vancouver of three different construction types: single-family wood-frame houses, low-rise unreinforced masonry buildings, and high-rise concrete frame structures with concrete shear walls. The two methodologies predict damage that lies in the same general damage categories of light and moderate, which are defined by fairly broad ranges in mean damage factors. The specific mean damage factors predicted by the two methods for a given location are significantly different, however. The significant differences in mean damage factors imply significant differences in damage costs and hence in seismic risk.Key words: earthquake, damage, seismic risk, vulnerability, modified Mercalli intensity (MMI), spectral response, displacement, acceleration.

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 score0.416
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.032
GPT teacher head0.274
Teacher spread0.243 · 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

Citations16
Published2006
Admission routes3
Has abstractyes

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