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Record W3000594003 · doi:10.1139/cjce-2018-0667

Seismic fragility assessment of multi-span concrete highway bridges in British Columbia considering soil–structure interaction

2020· article· en· W3000594003 on OpenAlexaffvenueabout
A. H. M. Muntasir Billah, M. Shahria Alam

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaLakehead University
Fundersnot available
KeywordsFragilitySeismic riskSeismic hazardBridge (graph theory)Incremental Dynamic AnalysisAbutmentVulnerability assessmentEarthquake engineeringVulnerability (computing)Geotechnical engineeringGeologyStructural engineeringEngineeringSeismic analysisForensic engineeringSeismologyComputer science

Abstract

fetched live from OpenAlex

Fragility curve is an effective tool for identifying the potential seismic risk and consequences during and after an earthquake. Recent seismic events have shown that bridges are highly sensitive and vulnerable during earthquakes. There has been limited research to evaluate the seismic vulnerability of the existing bridges in British Columbia (BC), which could help in the decision-making process for seismic upgrade. This study focuses on developing seismic fragility curves for typical multi-span continuous concrete girder bridges in BC. Ground motions compatible with the seismic hazard were used as input excitations for vulnerability assessment. Uncertainties in material and geometric properties were considered to represent the bridges with similar structural characteristics and construction period. The fragility of the bridge is largely attributable to the fragilities of the columns, and to a lesser extent, the abutment and bearing components. The results of this study show that, although not very significant, the soil–structure interaction has some effect on the component fragility where this effect is not very significant at the bridge system level.

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 score0.555
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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

Citations27
Published2020
Admission routes3
Has abstractyes

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