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Record W7135972484

Tensile mechanical response of basalt TRM composites exposed to elevated temperatures

2024· article· en· W7135972484 on OpenAlexfundno aff
Amrita Milling, Giuseppina Amato, Stephen Emerson, Luke Rea, Su; id_orcid 0000-0003-4796-1928 Taylor

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsUltimate tensile strengthTensile testingDigital image correlationComposite numberYoung's modulusBasalt fiberDurability
DOInot available

Abstract

fetched live from OpenAlex

The tensile behaviour of textile-reinforced mortar (TRM) composites and their effectiveness in reinforcingstructures in high-temperature environments remain unknown. This area lacks research because it is a relatively new field, and research efforts have initially focused on understanding their behaviour at ambient temperatures. This study investigated the tensile performance of basalt TRM composites (BTRM) made with short glass fibres at temperatures ranging from 21 to 4000C.Characterisation of the basalt textile (grid) at 21-2000C was also carried out. Samples were heated for one hour at constant temperatures in a furnace, allowed to cool naturally, and then tested at room temperature. Tensile tests were performed with a universal testing machine, and the digital image correlation technique was employed to measure elongation. Elevated temperatures generally led to degradation of the BTRM mechanical properties, including reduced tensile strength, modulus of elasticity, and ultimate strain. At 4000C, the composite had severely deteriorated mechanical functionality. The grid retained at least 90% of its room temperature tensile strength up to 200°C. However, it could not be tested at higher temperatures due to the loss of structural form. The performance and durability of TRM and its constituents need to be improved for use in high-temperature applications beyond 2000C.

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 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 score0.105
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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