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Record W4316810419 · doi:10.18280/acsm.460604

Ultrasound Non-Destructive Characterization of Early Hydration of Cement Pastes: The Effects of Water-Cement Ratio and Curing Temperature

2022· article· en· W4316810419 on OpenAlexvenueno aff
Hicham Banouni, Nabil Khatib, El Houssaine Ouacha, Bouazza Faiz, Idris Aboudaoud, Hicham Mesbah

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsCementMaterials scienceCuring (chemistry)Composite materialPortland cementModulusYoung's modulusUltrasonic sensor

Abstract

fetched live from OpenAlex

A newly developed ultrasound method that acquires at the same time both reflected and transmitted P-waves, at different angles, using two immersion transducers of 0.5 MHz central frequency, is described in this present study. This non-destructive method allows calculating the longitudinal and transverse velocities, and thus, the Young modulus, so that its evolutions is followed in time domain. The closely correlation between the evolution in time domain of those calculated parameters and hydration properties of cement based materials was used to characterize the effects of different water-cement ratio and curing temperatures on early age hydration behaviour of cement pastes. To do so, cement samples were prepared by mixing Portland cement and freshwater. Results indicates that lower water to cement ratio reduces the workability and increases the Young modulus of resulted cement medium. Also, both ultrasound velocities and Young modulus values increases linearly with increasing curing temperature.

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 score0.072
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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