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Record W2767756074 · doi:10.2495/safe-v7-n4-568-576

Approaches for evaluating failure probability of emergency power supply systems in hospitals

2017· article· en· W2767756074 on OpenAlexvenueno aff
Chi-Hao Lin, Xiaoqin Liu, Yi-Kuang Pan, Yu-Chia Liao

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsnot available
FundersMinistry of Science and Technology, Taiwan
KeywordsReliability engineeringComputer scienceMedical emergencyRisk analysis (engineering)EngineeringMedicine

Abstract

fetched live from OpenAlex

The unpredictable feature of earthquake is one of the major reasons for building damages and casualties occurring in earthquake events. Despite earthquake-induced structural damages to buildings, ground shaking can also impose critical effects on nonstructural components and systems in buildings. This study aimed to develop a probability-based approach for evaluating seismic damages to emergency power supply systems (EPSSs) in Taiwanese hospitals. For this purpose, four case hospitals that were somewhat damaged in the Chi-Chi earthquake (September 21, 1999) were investigated, and the component scoring system developed by the Multidisciplinary Center for Earthquake Engineering Research in the United States was referenced as the basis for developing seismic fragility curves for the components in the EPSSs of the case hospitals. Additionally, the logic tree method was employed to evaluate the failure probability of the EPSSs. The results exhibited acceptable consistency with the recorded damage status of the investigated systems, thereby verifying the feasibility of the proposed approach.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.396

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 head0.017
GPT teacher head0.253
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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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