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Record W4408135364 · doi:10.7734/coseik.2025.38.1.27

Assessment of Disaster Resilience Performance of Structural Systems in relation to Design Response Spectrum

2025· article· en· W4408135364 on OpenAlexaboutno aff
Taeyong Kim, Sang‐ri Yi, J.H. Kim, Ji‐Eun Byun

Bibliographic record

VenueJournal of the Computational Structural Engineering Institute of Korea · 2025
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Disaster responseRelation (database)Spectrum (functional analysis)Computer scienceEmergency managementData miningPolitical sciencePhysics

Abstract

fetched live from OpenAlex

The system-reliability-based resilience analysis facilitates the investigation of the joint capability of structural components to resist system failures. This approach has recently been extended to evaluate the resilience of structures subjected to stochastic excitations, such as earthquakes, based on the concept of reliability and redundancy curves. The reliability and redundancy curves are evaluated in a manner similar to traditional seismic fragility curves and are, therefore, capable of capturing the site-to-site variability of hazard characteristics, such as frequency content and duration. To quantitatively examine these aspects, the present study conducts a comparative analysis of two structures with identical designs but located at two different sites: Gyeongju, Korea, and Vancouver, Canada. The excitation properties at the two sites, which exhibit different frequency characteristics, are described using site-specific design response spectrum curves. Subsequently, a set of ground motion acceleration time histories is generated using a spectrum-compatible ground motion modeling approach. The generated ground motion sets for each site are applied to a 6-story frame structure, and a resilience analysis is performed. The numerical study demonstrates that the system-reliability based resilience analysis successfully captures site-specific characteristics and that these characteristics can significantly influence resilience evaluation results.

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 score0.457
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.243
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Computational Structural Engineering Institute of KoreaSame topicInfrastructure Resilience and Vulnerability AnalysisFrench-language works237,207