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Record W1987921082 · doi:10.1139/l09-169

Innovative design approach of precast–prestressed girder bridges using ultra high performance concrete

2010· article· en· W1987921082 on OpenAlexaffvenueabout
Husham Almansour, Zoubir Lounis

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPrecast concreteGirderStructural engineeringDurabilityPrestressed concreteEngineeringStructural loadSlabMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The construction of new bridges and the maintenance and renewal of aging highway bridge network using ultra high performance concrete can lead to the construction of long life bridges that will require minimum maintenance resulting in low life cycle costs. Ultra high performance concrete (UHPC) is a newly developed concrete material that provides very high strength and very low permeability to aggressive agents such as chlorides from de-icing salts or seawater. Ultra high performance concrete could enable major improvements over conventional high performance concrete (HPC) bridges in terms of structural efficiency, durability, and cost-effectiveness over the long term. A simplified design approach of concrete slab on UHPC girders bridge using the Canadian Highway Bridge Design code and the current recommendations for UHPC design is proposed. An illustrative example demonstrates that the use of UHPC in precast–prestressed concrete girders yields a more efficient design of the superstructure where considerable reduction in the number of girders and girder size when compared to conventional HPC girders bridge with the same span length. Hence, UHPC results in a significant reduction in concrete volume and then weight of the superstructure, which in turn leads to significant reduction in the dead load on the substructure, especially for the case of aging bridges, thus improving their performance.

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 score0.106
Threshold uncertainty score0.837

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 head0.017
GPT teacher head0.190
Teacher spread0.173 · 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

Citations19
Published2010
Admission routes3
Has abstractyes

Explore more

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