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Record W4281726617 · doi:10.1139/cjce-2021-0378

Finite element-based parametric and forensic analysis of corrosion-induced structural deterioration in prestressed concrete bridge girders

2022· article· en· W4281726617 on OpenAlexaffvenueabout
Liying Huang, Jiadaren Liu, Zhaohan Wu, Douglas Tomlinson, Carlos Cruz-Noguez, John H. Alexander, Yong Li

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsMinistry of Transportation of OntarioUniversity of Alberta
Fundersnot available
KeywordsGirderStructural engineeringCorrosionParametric statisticsFinite element methodPrestressed concreteFlexural strengthEngineeringMaterials scienceGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

To study corrosion effects on real structures, three prestressed concrete (PC) girders salvaged from a decommissioned 27-year-old bridge in Alberta, Canada, were studied numerically and compared to experimental tests. Nonlinear finite element (FE) models were developed to simulate the degraded flexural and shear behaviour of PC girders. The FE models were used to infer the possible unknown deterioration conditions (i.e., forensic analysis) by comparing FE-predicted and experimental behaviour of the girders. Based on the developed FE models, comprehensive parametric studies considering various corrosion-induced deteriorations at a wide range of corrosion levels were also conducted. Insights gained from the parametric study assisted assessing other girders under similar conditions. It was found that corrosion defects, such as reinforcement area loss, prestress force loss, and bonding loss in the end anchorage, affect PC girder behaviour significantly and the developed FE models of corroded PC girders can assist girder condition assessment.

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 score0.107
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.012
GPT teacher head0.204
Teacher spread0.191 · 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 routes3
Has abstractyes

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