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Record W7070378608

Performance of hot-mix asphalt mixtures for a balanced mix design

2024· dissertation· en· W7070378608 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsRutAsphaltCrackingAggregate (composite)Asphalt pavementDurabilityFatigue cracking
DOInot available

Abstract

Several transportation agencies in Canada are currently relying on volumetric properties of asphalt mixtures to accept or reject the final mix design. The existence of various pavement defects on Canadian roads indicates that volumetric mix design procedure alone does not guarantee adequate long-term pavement performance. Therefore, transportation agencies in Manitoba are finding ways to increase durability of their asphalt mixtures to accomplish a road network that is more sustainable, safer, and more economical. The balanced mix design (BMD) approach integrates two or more performance test criteria into mix design and acceptance to produce asphalt mixtures that are resistant to cracking and permanent deformation. The objective of this study is to assess cracking and rutting performance of plant-produced asphalt mixtures as well as fractionated reclaimed asphalt pavement (RAP) mixtures to validate current volumetric mix design methods and investigate ways to optimize mix performance for moving towards a BMD. Fractionated RAP mixtures were produced in the laboratory and plant-produced mixtures were collected from different pavement construction projects to prepare specimens for cracking and rutting evaluation. Cracking performance was determined using the Illinois flexibility index test and rutting performance was determined using the Hamburg wheel-tracking test. Results showed that polymer-modified binders, recycled materials, and reduction of nominal maximum aggregate size contributed to better rutting performance. In addition, RAP fractionation technology showed a promising effort in making asphalt mixtures move towards a BMD. Conversely, limestone aggregates and recycled asphalt shingles reduced cracking resistance and did not lead to a BMD.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: about_only · design weight: 3321.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Engineering dissertation on balanced mix design performance of hot-mix asphalt.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

It evaluates asphalt mixture performance, not research practice.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Civil engineering performance tests of hot-mix asphalt for balanced mix design.

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)0.001
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 score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMspace (University of Manitoba)Same topicAsphalt Pavement Performance EvaluationFrench-language works237,207