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Record W6891690266 · doi:10.48336/vb1a-e353

Influence of modifiers, anti-stripping agents and fillers on rheological and mechanical performance of asphalt mastic and asphalt mixture

2023· article· en· W6891690266 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAsphaltRheologyCreepRutUltimate tensile strengthFiller (materials)Dynamic shear rheometerCracking

Abstract

fetched live from OpenAlex

Filler, a fine powder used in asphalt mixture, plays a dual role as an inert filler to fill gaps between mineral aggregates and an active filler to mix with asphalt binder to generate a high-consistency asphalt mastic. Many studies have been conducted to develop a rheological parameter that can assess the deformation and creep characteristics of asphalt binders and asphalt mastics. This study investigated the creep recovery performance of asphalt binder and mastic. Mastic is prone to distresses in flexible pavement that worsen with aging, including cracking and moisture-induced damage. The study highlights the importance of fillers combined with modifiers and anti-stripping agents and compares the rheological and mechanical performance of aged asphalt mastics and asphalt mixtures. Multiple Stress Creep Recovery (MSCR) was utilized to understand the rutting performance of aged asphalt binder and mastic. The performance of asphalt mastic with different filler-binder ratios or proportions of different fillers combined with SBS or Gilsonite containing Zycotherm or AD-Here was utilized. Rolling Thin-Film Oven (RTFO), protocol was applied to simulate asphalt production time aging. The study utilized various parameters such as non-recoverable creep compliance, stress sensitivity analysis, percent recovery analysis, and polymer modification curve to compare the performance of the binders and mastics. Scanning Electron Microscope (SEM), and X-ray fluorescence spectroscopy test (XRF), were carried out to shed light on the physical and chemical properties of the fillers. The Marshall stability and flow test, Indirect Tensile Strength (ITS), and Retained Marshall Stability tests were performed to elucidate the mixtures’ mechanical performance and moisture susceptibility. Finally, ANOVA analysis was conducted at the binder, mastic, and mixture level to determine the factors influencing the rutting performance of asphalt mastics and the mechanical performance of asphalt mixtures. According to the experimental data from binder level analysis, 0.1% Zycotherm as an anti-stripping agent modified with 4% SBS satisfied binder performance requirements. Mastic and mixture level analysis suggested that HL0.5 modified with 4% SBS containing 0.1% Zycotherm was predominant when only active or inert filler is used and 10% HL and 70% LS containing 4% SBS, and 0.1% Zycotherm was predominant when a combination of active and inert filler was used. These mastics satisfied all the requirements for rutting, moisture damage, and cracking resistance. However, the combination of active and inert filler (10% HL + 70% LS) performed slightly better than the mastic prepared with only active filler (HL). The findings highlight the importance of fillers, modifiers and anti-stripping agents in enhancing the rutting and moisture-induced damage resistance of asphalt mixtures and the usefulness of the MSCR test in evaluating the performance of the asphalt mastic.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.037
GPT teacher head0.258
Teacher spread0.221 · 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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