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Record W4410174294 · doi:10.1002/eqe.4371

Seismic Resilience Assessment of a Regional Bridge Network

2025· article· en· W4410174294 on OpenAlex
Vahid Aghaeidoost, A. H. M. Muntasir Billah

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2025
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFragilityBridge (graph theory)Resilience (materials science)Vulnerability (computing)Reliability (semiconductor)Vulnerability assessmentEngineeringComputer scienceCivil engineeringPsychological resilienceComputer security

Abstract

fetched live from OpenAlex

ABSTRACT This study evaluates the seismic resilience of a regional bridge network, focusing on the interconnectedness and interdependence among individual bridges and emergency facilities. A framework for resilience assessment is developed and applied to a bridge network in Vancouver, British Columbia, consisting of 11 bridges across seven main routes. The methodology integrates fragility‐based models to calculate network resilience, considering various bridge configurations, including monolithic and seismically isolated bridges. The analysis highlights that bridges with seismic isolation exhibit superior resilience and functionality compared to monolithic bridges, especially under higher seismic intensities. Network damage, reliability, and resilience indices are used to quantify the impact of individual bridge failures on overall network performance. The results demonstrate the importance of bridge type and network topology on resilience, emphasizing that increasing alternative paths between critical nodes enhances network reliability and reduces vulnerability. These findings offer valuable insights for disaster mitigation strategies and infrastructure resilience planning in seismic‐prone regions.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.481
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.233
Teacher spread0.228 · 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