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Record W4382700904 · doi:10.11159/iccste23.195

Aging Concrete Slab on Steel Girder Bridges in Changing Climate

2023· article· en· W4382700904 on OpenAlexafffundvenueabout
Istemi F. Ozkan, Husham Almansour, Shahroz W. Shaikh

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council Canada
FundersInfrastructure CanadaNational Research Council Canada
KeywordsSlabGirderStructural engineeringEngineeringForensic engineeringGeology

Abstract

fetched live from OpenAlex

Concrete Slab on Steel Girder (CSSG) bridges are a common type of bridge in Canada, constituting 25% of the inventory of certain bridge owners.These bridges have typically exceeded half of their design life.However, bridge owners currently lack practical tools for predicting the residual capacity of their aging assets.In light of the changing climate, it is imperative to have the ability to estimate the residual capacity of these bridges for various loading conditions and failure modes.This paper provides a brief overview of the future research that the National Research Council Canada's Construction Research Centre plans to undertake over the coming years on the prediction of residual capacity and structural behaviour of aging CSSG bridges.The research will focus on the residual shear and bearing capacity of deteriorated steel girders, the impact of axial load on moment resistance, and the effects of deterioration on dynamic behaviour.The outcomes of this research are expected to provide Canadian bridge owners with practical tools for evaluating the residual capacity of their aging CSSG bridges, facilitating informed, data-driven decision-making.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.540

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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
Published2023
Admission routes4
Has abstractyes

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