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Record W2940167186 · doi:10.29173/mocs4

Prefabricated Timber Concrete Composite Floors

2016· article· en· W2940167186 on OpenAlexafffundvenue
Thomas Tannert

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBerner FachhochschuleUniversity of British ColumbiaFPInnovationsKommission für Technologie und Innovation
KeywordsPrecast concreteFormworkModular designCross laminated timberComposite numberCivil engineeringStructural engineeringEmbodied energyStiffnessArchitectural engineeringEngineeringEnvironmental scienceComputer scienceForensic engineering

Abstract

fetched live from OpenAlex

Timber-concrete-composite (TCC) floors are an example of hybrid systems that integrate different materials to achieve superior performance than the individual materials can provide. The structural and non-structural benefits of TCC floors over generic timber floors include increased capacity and stiffness, hallower depth, improved sound protection, and superior fire performance. Rapid erection due to the use of the timber as formwork, more economical gravity systems and foundations due to lighter weight, lower embodied energy, and reduced carbon footprint are advantages when comparing TCC systems to concrete slabs. This paper gives an overview of recent developments including research carried out on three novel solutions: 1) the äóìSwiss-Wood-Concrete-Flooräó�; 2) adhesively bonded TCC; and 3) äóìFT connectorsäó� that are embedded in precast concrete. All three solutions are well suited for modular and off-site construction and show the potential of using wood products beyond current limitations.

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 score0.276
Threshold uncertainty score0.800

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 head0.007
GPT teacher head0.170
Teacher spread0.163 · 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

Citations2
Published2016
Admission routes3
Has abstractyes

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