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Record W6995605775

Optimization of the Use of Post-Industrial Recycled Multilayer Plastic Packaging (MPP) as Asphalt Modifier

2023· dissertation· en· W6995605775 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltAggregate (composite)Activity-based costingPlastic wasteAsphalt pavementLife cycle costingDurabilityFatigue cracking
DOInot available

Abstract

fetched live from OpenAlex

The quality of road infrastructure significantly impacts the safety and comfort of road users and the country's economy. In Canada, 15% of roads are rated poor or very poor, with 108,000 km in poor condition, costing drivers CAD 3 billion annually. The cost of pavement maintenance and repairs can be high while investing in sustainable pavement design and maintenance is crucial to ensure longevity and safety. Researchers have investigated using thermoplastic additives, including recycled plastics, to improve pavement performance. Using multi-layer packaging plastics (MPP) additives has become a potential solution to enhance asphalt pavement materials. The primary components of MPP are Polyethylene (PE), Polyester (PET), Nylon (NY), and Metalized Polyester (METPET). Incorporating MPPs into asphalt mixtures minimizes plastic waste generation while conserving virgin aggregate and asphalt cement. The MPP stream from the plastic industry can contribute significantly to this endeavour, allowing for a more controlled and superior output than post-consumer plastics. This approach provides a sustainable solution for both the plastic and asphalt industries with the added benefit of enhanced pavement performance. \nIn Canada and the rest of the world, MPP waste is a growing concern due to its increasing volume over the past two decades, resulting in significant environmental and economic consequences. Recent studies have shown that the individual components of MPP, such as PE and PET, can be successfully used as asphalt modifiers, highlighting the potential for MPP to be used as an asphalt additive. However, a comprehensive study is lacking to evaluate MPP as a viable asphalt additive. This study aims to address this gap in the literature by evaluating the feasibility of using MPP as an asphalt modifier through wet and dry methods., Several sub-objectives were set to achieve this objective. These sub-objectives included the development of a laboratory-scale method for producing MPP powder, which can provide significant benefits for research, development, and education. The study then evaluates the physical, thermal, rheological, and storage properties of the MPP-modified binder at different MPP concentrations in asphalt cement (PG 58–28). The following tests were used: Differential Scanning Calorimeter (DSC), Thermogravimetric Analysis (TGA), Superpave Dynamic Shear Rheometer (DSR), Rotational Viscosity (RV), and Environmental Scanning Electron Microscopy (ESEM). The results from thermorheological testing indicate that incorporating MPP has a solid potential to improve permanent deformation resistance at high temperatures. Concentrations of less than 4% of MPP additives also offer adequate permanent deformation. \nThe study also uses the wet method to investigate the effect of MPP additives on the low-temperature performance of asphalt binders and mixtures. As thermal cracking may have a significant impact on the structural integrity of asphalt pavements, the following tests were used to evaluate the low-temperature properties of modified materials: Thermal Gravimetric Analysis, Differential Scanning Calorimeter, Dynamic Shear Rheometer, Bending Beam Rheometer (BBR), Tensile Strain Restrained Specimen test (TSRST), and Complex Modulus test. It was observed that the chemical composition of MPP influenced the asphalt binder's physical and performance properties. At low temperatures, results show that MPP additives at all dosages increase stiffness, affecting the Superpave Continuous PG and BBR ΔTc parameter. While MPP additives can increase asphalt mixtures' stiffness, they may also reduce their resistance to thermal cracking, demonstrating that the MPP modification percentage should be limited to below 4% by the weight of the binder. \nMoreover, the study investigated the potential of using a high concentration of MPP pellets to enhance asphalt mixtures using the dry method. The following tests were used: Complex Modulus Test, Moisture-Induced Damage Test, British Pendulum Skid Resistance Tester, Indirect Tensile Cracking Test (Ideal-CT), and Hamburg Wheel Tracking Test (HWTT). The results show that incorporating MPP additives significantly improves resistance to softening at higher temperatures, fracture resistance, rutting resistance, load-carrying capacity, and skid resistance while reducing susceptibility to moisture damage. A more sustainable solution can be promoted by incorporating MPP additives into asphalt mixtures using wet and dry methods. \nThe study highlights the need for increased efforts to address the growing MPP waste issue and promote sustainability and circular economy principles. The research has demonstrated that MPP waste can be upcycled to reduce plastic waste and conserve virgin materials such as asphalt cement, aggregate and plastic additives. The study's findings provide valuable information for policymakers and industries to develop sustainable strategies and regulations to address the MPP waste issue. Thus, repurposing plastic waste and promoting a circular economy is possible through a holistic approach and stakeholder collaboration. However, further research and testing are needed to determine MPP additives' long-term durability and effectiveness in asphalt mixtures under various environmental and traffic conditions. In summary, this study offers valuable insights into the potential of repurposing plastic waste to enhance pavement performance through a comprehensive evaluation of MPP as an asphalt modifier. The laboratory-scale method for producing MPP powder presents significant benefits for research and education.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.024
GPT teacher head0.196
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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