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Record W2744053188 · doi:10.1139/cjce-2016-0555

Vulnerability assessment of seismic induced out-of-plane failure of unreinforced masonry wall buildings

2017· article· en· W2744053188 on OpenAlexaffvenueabout
Ahmad Abo El Ezz, Clémentine Houalard, Marie‐José Nollet, Rola Assi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à MontréalGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsUnreinforced masonry buildingStructural engineeringDisplacement (psychology)MasonryVulnerability assessmentVulnerability (computing)Seismic riskGeologyGeotechnical engineeringEngineeringSeismologyComputer science

Abstract

fetched live from OpenAlex

Damage to unreinforced masonry (URM) buildings from earthquake shaking is often caused by out-of-plane failure of walls. This is particularly relevant to the majority of URM buildings in Eastern Canada that were constructed prior to the introduction of seismic design prescriptions. Seismic vulnerability assessment of this type of failure is therefore an essential step towards seismic risk mitigation. This paper presents a simplified procedure for seismic vulnerability assessment of out-of-plane failure of URM wall buildings. The procedure includes the development of an equivalent single degree of freedom model of the wall with a characteristic force–deformation capacity curve. The capacity curve is convolved with displacement response spectrum to predict the displacement demand. The predicted displacement demand is compared to displacement thresholds criteria corresponding to the initiation of each damage state. The procedure is applied to an inventory of URM buildings in Montreal and the corresponding probability of out-of-plane damage is evaluated.

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 score0.099
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.212 · 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
Published2017
Admission routes3
Has abstractyes

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