Systematic Literature Review For The Use Of Polypropylene As Stabilization Or Reinforcement Material For Road Paving Bases And Sub-Bases
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".