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Record W7115721024 · doi:10.71846/18-wcee-1018

EVALUATING THE SEISMIC PERFORMANCE OF BRIDGES IN METRO VANCOUVER CONSIDERING DEEP BASIN EFFECTS

2025· article· en· W7115721024 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Ground motionStructural basinSubductionInduced seismicityStiffnessSeismic riskResilience (materials science)

Abstract

fetched live from OpenAlex

This study is a part of the broader project aimed at assessing the seismic resilience of the transportation network in Metro Vancouver, BC. This paper evaluates the seismic performance of bridges within the Metro Vancouver under plausible M9 Cascadia subduction zone (CSZ) earthquakes using a simplified modeling approach. An inventory of more than 200 bridges was assembled by collecting as-built drawings. The detailed properties of approximately 80 bridges were extracted, with the focus on bridges supported by reinforced concrete (RC) circular columns and rectangular walls. By leveraging this information, simplified Single-Degree-of-Freedom (SDOF) bridge models were developed to characterize the mass, stiffness and strength of each bridge. Nonlinear time history analyses were carried out to evaluate the response of each bridge under 30 physic-based ground motion simulations of M9 CSZ earthquakes adjusted for the corresponding site conditions, which explicitly capture the amplification effects of the Georgia Sedimentary Basin. The results indicate that bridge damage correlates well with basin depth, with 33%, 48% and 92% of probability of complete damage, on average, for bridges outside the basin, in the basin edge and in deep-basin sites, respectively. While modern bridges perform considerably better in outside-basin sites, their performance is comparable to older bridges at basin-edge and deep-basin locations.

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 score0.047
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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