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Record W4409799993 · doi:10.11159/icgre25.202

Systematic Literature Review For The Use Of Polypropylene As Stabilization Or Reinforcement Material For Road Paving Bases And Sub-Bases

2025· article· en· W4409799993 on OpenAlexvenueno aff
Vivian Silveira dos Santos Bardini

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersUniversidade Estadual de CampinasCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPolypropyleneReinforcementComputer scienceForensic engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

This article addresses the use of plastic waste of the polypropylene type in civil construction in infrastructure works, especially as a material for reinforcing and stabilizing soil in the bases and sub-bases of paving intended for road transport.Through a systematic bibliographical review and with the aim of showing the state of the art about this practice, it can be understood that plastic waste can be used as aggregates or additions to the soil to stabilize or reinforce bases and sub-bases of floors.Contributions were found on methods, laboratory testing procedures, type and format of materials as well as their respective compositions and dosages.The results obtained in the studies analyzed showed discoveries and indications and limitations to the use of this material in its various formats.This work is important because, given the growing search for sustainable solutions to reduce the environmental impacts of plastic waste, it contributed by presenting research in several countries and continents with significant gains in physicalmechanical properties with reports of experiences that achieved improvements of up to 200% in compressive strength of unconfined soil, among other indicators of improvements in the original soils.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.221
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 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207