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Record W4281714600 · doi:10.18280/acsm.460201

Influence of Recycled Plastic Waste and Cement on Pavement Sub-Base Stabilization

2022· article· en· W4281714600 on OpenAlexvenueno aff
Hocine Ziani, Sadek Deboucha, Abderrachid Amriou, Hayat Touati, Inès Kebaili

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCementPlastic wasteWaste managementBase (topology)Environmental scienceBase courseForensic engineeringGeotechnical engineeringMaterials scienceEngineeringComposite materialAsphalt

Abstract

fetched live from OpenAlex

The use of recycled waste in road construction is part of the sustainable development of countries. Plastic waste poses an ecological problem because it is not biodegradable. Recycling this waste to reuse it in different areas seems to be a solution to reduce it. This article is devoted to the use of varieties of additions of recycled plastic waste (RPW) (5 and 10%) without and with Portland cement compound (PCC) (2 and 4%) moistened at the Proctor optimum, to stabilize the foundation layer of the section of the road connecting the highway at the Tixter dry port (Algeria). The results showed, that RPW increases the CBR (California Bearing Ration) of S0 approximately to 139.32%, however the use of 5% of RPW with 2% of PCC raises the CBR of the soil to 386.59% in unsoaked samples and 404.54% in soaked samples. Whereas, UCS (Unconfined Compressive Strength) tests, is marked by increases in compressive strength from 0 to1471.18 KPa and from 754.67KPa to 2051.53KPa of samples dosed with 5% RPW and 4% PCC, soaked and unsoaked respectively. This study offers the opportunity to find the right soil-RPW-PCC combination to stabilize future roads.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.247
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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