MétaCan
Menu
Back to cohort
Record W4400235029 · doi:10.11159/iccste24.263

Flexural Assessment of Existing Slab Bridges in The Pacific Northwest Region Under Long-Duration Earthquake Effects

2024· article· en· W4400235029 on OpenAlexvenueno aff
Shaymaa Obayes, Monique Head

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSlabDuration (music)Flexural strengthGeologySeismologyForensic engineeringStructural engineeringEngineeringGeophysicsAcoustics

Abstract

fetched live from OpenAlex

Previous research has been conducted on the vulnerability of bridges, but there is a gap in assessing the vulnerability of existing bridges, such as slab bridges, when subjected to long-duration earthquakes in regions with high seismic activity.This study provides a unique quantification and assessment of the impacts of an anticipated moment magnitude (MW) 9.0 earthquake event characterized by its long-duration on the incipient collapse risk of slab bridges in the Pacific Northwest (PNW) region.The assessment encompasses the potential for flexural failures in the columns of slab bridges and the risk of collapse.A slab bridge is modeled in OpenSees as case studies to quantify the vulnerability and risk of incipient collapse using fragility analyses and risk-targeted approach per the 2023 AASHTO Guide Specifications for LRFD Seismic Bridge Design.This study highlights the consequences of the lack of strict seismic design standards in older design codes, especially for slab bridges built before the 1990s.In addition, the findings of this research have shown the impact of long-duration earthquakes on their potential to drastically increase collapse risks of aging slab bridges built before the 1990s.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.315

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 integrity0.0000.000
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicSeismic Performance and AnalysisFrench-language works237,207