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Conformable Tire Patch Loading for FRP Composite Bridge Deck

2009· article· en· W2077139659 on OpenAlexaff
Prasun Majumdar, John J. Lesko, Thomas E. Cousins, Zihong Liu

Bibliographic record

VenueJournal of Composites for Construction · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Calgary
FundersVirginia Department of Transportation
KeywordsFibre-reinforced plasticDeckStructural engineeringMaterials scienceConformable matrixDurabilityComposite materialComposite numberEngineering

Abstract

fetched live from OpenAlex

Fiber-reinforced polymer (FRP) composites are increasingly being used in bridge deck applications. However, there are currently only fledgling standards to design and characterize FRP deck systems. One area that should be addressed is the loading method for the FRP deck. It has been observed that the type of loading patch greatly influences the failure mode of a cellular FRP deck. The contact pressure distribution of a real truck loading is nonuniform with more concentration near the center of the contact area as a result of the conformable contact mechanics. Conversely, the conventional rectangular steel patch on a FRP deck act like a rigid flat punch and produces stress concentration near the edges. A proposed simulated tire patch has been examined for loading a cellular FRP deck with the load distribution characterized by a pressure sensitive film sensor and three-dimensional contact analysis using ANSYS. A loading profile is proposed as a design tool for analyzing FRP deck systems for strength and durability. Local top surface strains and displacements of the cellular FRP deck are found to be higher with proposed loading profile compared to those for the conventional uniformly distributed loading. Parametric studies on the deck geometry show that the global displacement criterion used for characterizing bridge deck is inadequate for a cellular FRP deck and that the local effects must be considered.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.880

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 communication0.0000.001
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 head0.243
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 designBench or experimental
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

Citations16
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Composites for ConstructionSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207