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Strengthening of glued-laminated timber beams using externally bonded fiber reinforced polymer sheets and near surface mounted reinforcement

2025· article· en· W4411867132 on OpenAlexafffund
Jodie Goodwin, Joshua E. Woods

Bibliographic record

VenueConstruction and Building Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReinforcementMaterials scienceComposite materialFiberSurface (topology)Structural engineeringEngineeringGeometry

Abstract

fetched live from OpenAlex

This study examines the use of externally bonded fiber reinforced polymer (FRP) sheets and near-surface mounted (NSM) reinforcement to improve the flexural strength of glued-laminated (glulam) timber beams. Distributed fiber optic sensors (DFOS) were used to evaluate the distribution of strains over the length and depth of the beams as well as for quantification of the maximum strains in the reinforcing materials. The influence of FRP fiber type (glass or carbon), number of FRP layers, FRP anchorage detailing, and NSM bar type on flexural stiffness, strength, and ductility was assessed. NSM materials for flexural strengthening included both steel and titanium rebar which is a novel solution that, similar to FRP, is light-weight and corrosion-resistant. The results showed that the use of externally bonded FRP sheets on the tension side can promote a compression failure, avoiding brittle tension failure or FRP debonding and result in improvements in stiffness and strength of up to 160 % and 156 %, respectively when compared to the control. The addition of NSM reinforcement in the compression region was found to result in further increases in the beam flexural stiffness and strength by up to 248 % and 230 %, respectively when compared to the control. The distribution of strain and maximum strain achieved in the wood, FRP, and NSM reinforcement are discussed in the paper.

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 categoriesMeta-epidemiology (narrow)
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 score0.019
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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 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

Citations6
Published2025
Admission routes2
Has abstractyes

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