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Record W2967604373 · doi:10.5772/intechopen.88105

NDT Methods for Evaluating FRP-Concrete Bond Performance

2019· book-chapter· en· W2967604373 on OpenAlexaff
Kenneth Crawford

Bibliographic record

VenueIntechOpen eBooks · 2019
Typebook-chapter
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsInternational Institute for Sustainable Development
FundersWabash CollegeBall Brothers Foundation
KeywordsFibre-reinforced plasticNondestructive testingMaterials scienceBondStructural engineeringForensic engineeringComposite materialEngineeringPhysicsBusiness

Abstract

fetched live from OpenAlex

The long-term bond performance, 15+ years, of FRP-structural systems applied to reinforced-concrete structures is largely unknown and not widely tested. FRP-structural system performance is a function of FRP-concrete bond condition and is subject to deterioration over time. The purpose of this investigation is to test and validate the non-destructive testing impulse-excitation technique to evaluate bond condition of FRP systems applied to concrete structures, in particular concrete highway bridges. The objective is to identify changes in the FRP-concrete bond state by analyzing changes in impulse excitation (impact) frequencies and sinusoid waveforms. Hammer impact tests were performed on two FRP-retrofitted highway bridges in Missouri and a bonded FRP test plate in the laboratory. Signal analysis of recorded impact acoustic emissions was performed on frequencies and waveform damping ratios of bonded and de-bonded FRP material on two bridges and in the lab. The frequencies and sinusoidal waveforms of the bonded and de-bonded FRP material on the bridges had a high degree of correlation to those of the bonded/de-bonded laboratory FRP plate. This investigation confirms the impulse excitation technique to test FRP bond on concrete structures, which provides accurate data on the bonded versus de-bonded FRP-bond condition.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.917
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.336
Teacher spread0.289 · 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.

Study designOther design
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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