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Record W4399922798 · doi:10.18280/rcma.340311

Effect of Treatment with Different Classes of Cement on the Geotechnical Properties of Soils: Case Study of Red Soil in the M'sila Region, Algeria

2024· article· fr· W4399922798 on OpenAlexvenueno aff
Lakhdar Mekki, Ahmed Seddiki, Abderrachid Amriou, Larbi Belagraa

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2024
Typearticle
Languagefr
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSoil waterGeologyRed soilCementSoil scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

This article showcases the findings of an experimental study conducted on a red siltclayey taken from the site of Chaaba Elhamra (M'sila region, Algeria) for the purpose of being used for the building of road embankments and pavement layers.This experimental research aims to evaluate the geotechnical characteristics of this red soil, both before and after being treated with two classes of cement (CEM-II/B-L 32.5 N and CEM-II/B-L 42.5 N), and to analyse the impact of the cement class on its geotechnical properties.The experimental program included identification tests as well as Proctor compaction, CBR, and unconfined compression tests.The interpretation of the results has taken into account knowledge acquired from the literature.The findings showed that the percentage of cement had a beneficial effect on the geotechnical characteristics of this red silt-clay.Moreover, they highlighted that the type of cement did not have a great influence on the physical parameters, the compaction parameters, and the CBR indices.However, it was observed that class 42.5 cement had a much greater effect on UCS values than lower class 32.5 cement.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.059
GPT teacher head0.263
Teacher spread0.205 · 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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