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Record W4410079473 · doi:10.1680/jenge.24.00103

Material recovery from waste rubber tyres and their geoenvironmental utilisation: a review

2025· review· en· W4410079473 on OpenAlexaff
Prithvendra Singh, Myint Win Bo, Eng‐Choon Leong, Amin Soltani, Laura Banasiak, Brendan C. O’Kelly, Zhuoyuan Cheng, Than Than Nyunt, Gabriele Chiaro, Alessandro Palermo, Sara Rı́os, Castorina Silva Vieira, Rao Martand Singh, Devendra Narain Singh

Bibliographic record

VenueEnvironmental Geotechnics · 2025
Typereview
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsNatural rubberWaste managementEnvironmental scienceCivil engineeringWaste materialEngineeringForensic engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The escalating environmental challenges posed by waste rubber tyres (WRTs) necessitate innovative solutions to address their detrimental effects on the geoenvironment. Thus, the knowledge about the recent advancements in material recovery from WRTs, emphasising their utilisation within the framework of the United Nations Sustainable Development Goals (SDGs) and the circular economy principles, is the need of the hour. Keeping this in mind, various techniques generally used for material recovery, viz., ambient, cryogenic, waterjet, and so on, which unveil innovative approaches to reclaiming valuable resources (viz., recycled rubber, textiles, steel wires, etc.) from WRTs and various devulcanisation techniques (viz., physical, chemical, and microbial) are elaborated in this paper. In parallel, the paper explores the utilisation of the WRTs and recovered materials, highlighting their application in geotechnical and geoenvironmental engineering development projects while addressing the necessary environmental precautions and associated environmental risks/concerns. This paper incorporates circular economy principles into WRTs utilisation and focuses on achieving SDGs by promoting resource efficiency and minimising their environmental impact.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.196
Teacher spread0.188 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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