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Record W4220772908 · doi:10.1139/cjce-2021-0344

Seismic performance assessment of an existing multispan bridge in eastern Canada retrofitted with fiber reinforced elastomeric isolator

2022· article· en· W4220772908 on OpenAlexafffundvenueabout
Saber A.S. Fosoul, Michael J. Tait

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeismic retrofitBridge (graph theory)Structural engineeringIsolatorFragilityEngineeringGeotechnical engineeringDeformation (meteorology)GeologyReinforced concrete

Abstract

fetched live from OpenAlex

A seismically resilient transportation network entails preprioritized retrofit plans for conventionally designed highway bridges. This is particularly important for the province of Ontario in Canada, where more than 44% of the multispan bridges have been constructed prior to 1970. To support future seismic risk mitigation efforts, this study evaluated the seismic performance of a multispan continuous reinforced concrete bridge in Ontario, Canada, in its as-built and retrofitted conditions. Seismic retrofit is conducted utilizing novel Fiber Reinforced Elastomeric Isolators (FREIs). Analytical fragility curves are developed using Incremental Dynamic Analysis (IDA) on a three-dimensional nonlinear finite element model of the bridge using 45 synthetic ground motion records for eastern Canada. Results indicate that seismic isolation can effectively mitigate the seismic demand on columns and transfer the shear forces to the end abutments resulting in excessive backfill soil deformation. However, this deformation does not necessarily result in bridge failure and traffic disruption.

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 score0.501
Threshold uncertainty score0.878

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 integrity0.0000.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations8
Published2022
Admission routes4
Has abstractyes

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