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Record W1578800732 · doi:10.5539/jmsr.v4n3p33

Development of Effective Textile-Reinforced Concrete Noise Barrier

2015· article· en· W1578800732 on OpenAlexvenueno aff
Henrik Funke, Sandra Gelbrich, Lothar Kroll

Bibliographic record

VenueJournal of Materials Science Research · 2015
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
FundersAllianz Industrie ForschungDeutsche ForschungsgemeinschaftTechnische Universität Chemnitz
KeywordsFormworkMaterials scienceSlabContext (archaeology)Structural engineeringLoad bearingShell (structure)Bearing (navigation)TextileComposite materialNoise (video)Computer scienceEngineeringGeology

Abstract

fetched live from OpenAlex

Thin-walled, high-strength concrete elements exhibiting low system weight and great slenderness can be created with a large degree of lightweight structure using the textile-reinforced, load-bearing concrete (TRC) slab and a shell with a very high level of sound absorption. This was developed with the objective of lowering system weight, and then implemented operationally in construction. Arising from the specifications placed on the load-bearing concrete slab, the following took place: an adapted fine-grain concrete matrix was assembled, a carbon warp-knit fabric was modified and integrated into the fine concrete matrix, a formwork system at prototype scale was designed enabling noise barriers to be produced with an application-oriented approach and examined in practically investigations within the context of the project. This meant that a substantial lowering of the load-bearing concrete slab’s system weight was possible, which led to a decrease in transport and assembly costs.

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)0.007
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 score0.006
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.324
Teacher spread0.284 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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