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Record W2520031226 · doi:10.1139/cjce-2015-0428

Effect of critical sub-system failures on the post-earthquake functionality of buildings: A case study for Montréal hospitals

2016· article· en· W2520031226 on OpenAlexaffvenueabout
Suze Youance, Marie‐José Nollet, Ghyslaine McClure

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsMcGill UniversityÉcole de Technologie Supérieure
Fundersnot available
KeywordsFragilityVulnerability (computing)Fault tree analysisVulnerability assessmentComputer scienceUrban seismic riskQuality (philosophy)Construction engineeringCivil engineeringSeismic hazardEngineeringReliability engineeringComputer security

Abstract

fetched live from OpenAlex

When an earthquake occurs, hospitals are expected to remain functional as they play a crucial role in emergency care operations. This ability to ensure the continuity of quality operations while ensuring the safety of occupants during and after an earthquake defines the concept of post-earthquake functionality. Hospital functionality relies on the good performance of a large number of critical sub-systems, components and equipment. Although the global seismic performance of building structures and their nonstructural components was extensively observed in several post-disaster reconnaissance surveys, there is limited and incomplete information on the effect of building and nonstructural damage on post-earthquake functionality. The objective of this paper is to present a methodology for the assessment of post-earthquake functionality of existing Montréal hospitals using fault-tree analysis. The study shows that using specific and accurate information on the vulnerability and fragility of structural and critical nonstructural components, a probabilistic index of post-earthquake functionality of the entire facility is computed which informs mitigation action for the critical failure processes through the system.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations12
Published2016
Admission routes3
Has abstractyes

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