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Record W2110294598 · doi:10.1139/cjce-2013-0293

Adapting a rapid seismic screening method for the evaluation of school buildings

2014· article· en· W2110294598 on OpenAlexaffvenueabout
Helene Tischer, Denis Mitchell, Ghyslaine McClure

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsInduced seismicityCivil engineeringStrengths and weaknessesEngineeringConstruction engineeringStructural engineeringComputer scienceForensic engineering

Abstract

fetched live from OpenAlex

The poor seismic performance of schools has made their assessment and retrofit a priority in moderate and high seismic zones. Given the large building inventory to evaluate, rapid seismic screening methods are often implemented to prioritize detailed interventions. This paper describes schools’ specific characteristics to be considered when applying these procedures, and shows how the FEMA154 approach can be modified to consider them. The adapted method is a score assignment procedure based on the following six essential characteristics: seismicity, lateral load resisting system, construction year, potential structural weaknesses (or irregularities), potential for pounding of adjacent buildings, and local soil conditions. The method is illustrated with case studies, applied to 101 school buildings in Quebec. Results show that most of the parameters considered influence the final scores. In particular, the treatment of structural weaknesses and potential for pounding proved effective in differentiating the likely seismic performance of the buildings.

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)0.003
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.953
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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
Published2014
Admission routes3
Has abstractyes

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