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Record W2058848153 · doi:10.5539/jmsr.v4n1p36

Anisotropic fibre-reinforced plastics as formworks for single and double-curved textile reinforced concrete

2014· article· en· W2058848153 on OpenAlexvenueno aff
Henrik Funke, Sandra Gelbrich, Andreas Ehrlich, Lars Ulke‐Winter, Lothar Kroll

Bibliographic record

VenueJournal of Materials Science Research · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsFormworkMaterials scienceFibre-reinforced plasticCurvatureComposite materialBendingTextileAnisotropyUltimate tensile strengthDurabilityStructural engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

A new constructive and technological approach was developed for the efficient production of large-dimensioned, curved freeform formworks, which allows the manufacturing of single and double-curved textile reinforced concrete elements. The approach is based on a flexible, multi-layered formwork system, which consists of glass-fibre reinforced plastic (GFRP). Using the unusual structural behavior caused by anisotropy, these GFRP formwork elements permit a specific adjustment of defined curvature. The system design of the developed GFRP formwork and the concrete-lightweight-elements with stabilized spacer fabric was examined exhaustively. Prototypical curved freeform surfaces with different curvature radii were designed, numerically computed and produced. Furthermore, the fabric’s contour accuracy of the fabric was verified, and its integration was adjusted to loads. The developed textile reinforced concrete (TRC) had 3-point bending tensile strength of 41.51 MPa. Beyond that, it was ensured that the TRC had a high durability, which has been shown by the capillary suction of de-icing solution and freeze thaw test with a total amount of scaled material of 1172 g/m² and a relative dynamic E-Modulus of 100% after 28 freeze-thaw cycles.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.323
Teacher spread0.276 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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