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Record W2552046566 · doi:10.1080/1064119x.2016.1255688

Numerical investigation of the uplift performance of prestressed fiber-reinforced polymer floating piles

2016· article· en· W2552046566 on OpenAlexaff
Pengpeng Ni, Guoxiong Mei, Yanlin Zhao

Bibliographic record

VenueMarine Georesources and Geotechnology · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
FundersNational Outstanding Youth Science Fund Project of National Natural Science Foundation of ChinaNational Natural Science Foundation of China
KeywordsRebarEmbedmentMaterials scienceFiber-reinforced concreteParametric statisticsComposite materialFinite element methodStructural engineeringCarbon fiber reinforced polymerSlip (aerodynamics)Fibre-reinforced plasticPolymer concreteFiberReinforced concreteCementEngineering

Abstract

fetched live from OpenAlex

Finite element analyses of prestressed fiber-reinforced polymer floating piles subjected to uplift force have been conducted in this paper. First, parameters of the modified BPE model (bond–slip model at the fiber-reinforced polymer–concrete interface) were calibrated using existing pullout testing data on fiber-reinforced polymer rebars embedded in concrete. Nonlinear spring elements were used in numerical modeling to characterize the bond–slip behavior at the fiber-reinforced polymer–concrete interface. A parametric study was performed to assess the influence of rebar diameter, fiber-reinforced polymer material, embedment length, and concrete strength on the mobilized bond stress. Upon the successful modeling of the pullout performance of fiber-reinforced polymer rebars in concrete, numerical models were developed to investigate the dependence of the uplift performance of floating piles on the prestress level, uplift force, fiber-reinforced polymer type, and compressive strength of concrete.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.330

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 head0.003
GPT teacher head0.153
Teacher spread0.150 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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