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Record W4377023149 · doi:10.4028/p-71en7d

The Efficiency of Non-Destructive Testing to Estimate the Damage Level of Fiber-Reinforced Concrete Exposed to High Temperatures

2023· article· en· W4377023149 on OpenAlexaff
Abdelhamid Mejdoub Nefoussi, Mohammed Ezziane, Hocine Siad, Ramdane Chihaoui, Mohamed Mouli, Mohamed Lachemi, Ayed Kada

Bibliographic record

VenueMaterials science forum · 2023
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceComposite materialUltrasonic sensorNondestructive testingYoung's modulusTypes of concreteThermalCompressive strengthAcoustics

Abstract

fetched live from OpenAlex

The feasibility of determining the extent of damage in fibered concrete after being subjected to high temperatures, using non-destructive methods was investigated. The study was conducted on four concrete mixtures with different fiber types. The specimens underwent a curing process at 23 °C before being exposed to different high temperatures of 400 °C, 600 °C, and 800 °C. After cooling to ambient temperature, various non-destructive tests including ultrasonic pulse velocity testing (UPV), the resonance frequency test (RF), the dynamic modulus of elasticity (Ed), the thermal conductivity test (λ), and Schmid Rebound Hammer (SRH), were performed. To evaluate the sensitivity of non-destructive techniques to assess the damage of fiber-reinforced concrete, the Lemaitre coefficient was used as a variable to describe the extent of the damage. The results indicated that the highest damage levels were obtained through the modulus of elasticity technique regardless of the type of concrete mixture or temperature exposure. There was also a potential agreement found between thermal and ultrasonic methods in evaluating the thermal degradation of concrete.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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
Published2023
Admission routes1
Has abstractyes

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