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Record W2150492540 · doi:10.1139/l04-019

Performance assessment of FC girder bridges in Alberta

2004· article· en· W2150492540 on OpenAlexfundvenueaboutno aff
Nadeem Ahmad Khattak, J. J. Roger Cheng

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersGovernment of Alberta
KeywordsGirderPrecast concreteStructural engineeringDeckEngineeringBridge (graph theory)CrackingPrestressed concreteStructural loadGeotechnical engineeringSpan (engineering)Shear (geology)StiffnessCivil engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

A large number of precast prestressed concrete multi-girder bridges were constructed in Alberta in the early 1960s. Major benefits of this type of construction included the elimination of the cast in place concrete bridge deck and the accelerated pace of construction in erecting the bridge. However, after providing 10 to 20 years of service, some of these bridges started to form longitudinal cracks in the deck directly over the girder joining shear key locations. Once a crack was formed in the shear keys, salt and water would penetrate the wearing surface and weaken the shear key grout. This brought about concerns regarding adequate load sharing among the girders and corrosion of the prestressing tendons within the girders. An extensive survey was undertaken to observe the longitudinal cracking on these bridge decks. The objectives of the field survey were to look for possible trends or relationships between various influencing parameters and the performance of these bridges. Field data were obtained from a visually selected sample survey and a comprehensive bridge record survey based on Alberta Transportation (Government of Alberta) bridge files. The parameters investigated for influence on bridge performance were span length, span width, bridge skew, service age, and traffic volume. Finally, the rehabilitation schemes used on these bridges in the past are described, and the strengths and weaknesses of each rehabilitation strategy are discussed.Key words: bridges, concrete bridges, bridge girders, load sharing, rehabilitation, shear keys, assessment, traffic loads, lateral prestressing, transverse stiffness.

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 score0.642
Threshold uncertainty score0.796

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations2
Published2004
Admission routes3
Has abstractyes

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