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Record W2732294592 · doi:10.1193/091816eqs153m

Empirical Damage Relationships and Benefit‐Cost Analysis for the Seismic Retrofit of URM Buildings

2017· article· en· W2732294592 on OpenAlexaffabout
Brandon Paxton, Kenneth J. Elwood, Jason Ingham

Bibliographic record

VenueEarthquake Spectra · 2017
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsRead Jones Christoffersen (Canada)
FundersU.S. Geological SurveyNational Institute of Standards and Technology
KeywordsBracingMasonrySeismic retrofitUnreinforced masonry buildingForensic engineeringEngineeringSeismic hazardSeismic riskCivil engineeringDowntownPedestrianRetrofittingGeographyReinforced concreteStructural engineeringArchaeology

Abstract

fetched live from OpenAlex

Benefit‐cost analyses for the seismic retrofit of unreinforced masonry (URM) buildings in downtown Victoria, British Columbia, Canada, were undertaken, considering the seismic hazard, building value, occupant/pedestrian exposure, a variety of strengthening measures, and local construction costs. The analyses are underpinned by building motion‐damage relationships developed based on observed damage in past earthquakes in California and New Zealand. The considered upgrading measures ranged from parapet bracing to comprehensive seismic upgrades consistent with local practices. Parapet bracing and other partial retrofits were shown to have favorable benefit‐cost ratios and thus be strong candidate measures for risk mitigation programs. Full upgrades were shown to have less favorable benefit‐cost ratios. While applied to Victoria, the generality of the methodology and the use of observed damage data from California and New Zealand make the findings of this study particularly relevant for similar locations throughout the Pacific Northwest and abroad.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.402

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 head0.034
GPT teacher head0.271
Teacher spread0.236 · 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 designObservational
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

Citations7
Published2017
Admission routes2
Has abstractyes

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