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Record W4409799793 · doi:10.11159/icgre25.194

Investigation of Mechanical Properties of Hot Mix Asphalt Modified with Elvaloy and Polyphosphoric Acid

2025· article· en· W4409799793 on OpenAlexvenueno aff
Mehmet Yılmaz, Beyza Furtana Yalçın

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsAsphaltAsphalt pavementPulp and paper industryMaterials scienceComposite materialEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

In order for the bituminous hot mix layers to resist the increased traffic load and volume as well as the adverse environmental conditions for a long time without being deformed, the bituminous binder used in the mixture has been commonly modified with the Styrene-Butadiene-Styrene (SBS) polymer.The high demand for SBS makes it difficult to supply and therefore brings on the use of alternative additives.In this study, the combined use of Elvaloy and phosphoric acid (PPA) was investigated.Additionally, the effects on the mechanical properties of bituminous mixtures were examined.Modified binders were prepared using Elvaloy/PPA additives mixed under specified conditions.Hot mix asphalt (HMA) samples prepared with these binders were subjected to Marshall stability and flow, indirect tensile stiffness modulus, and indirect tensile fatigue tests to comprehensively evaluate the effects of modified binders on the mechanical properties of bituminous hot mixtures.As a result, the bituminous hot mix tests revealed that asphalt mixtures improved their mechanical properties at the specified ratios.

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
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.179
Teacher spread0.171 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207