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Record W3167835792 · doi:10.1061/9780784483510.027

Mechanical Properties of Asphalt Emulsion Stabilized Base Course Modified Using Portland Cement and Asphaltenes

2021· article· en· W3167835792 on OpenAlexaffabout
Muhammad Misbah Uddin, Farshad Kamran, Manjunath Basavarajappa, Nura Bala, Benjamin Corenblum, Leila Hashemian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphalteneAsphaltMaterials scienceUltimate tensile strengthCementPortland cementComposite materialEmulsionCrackingBase courseAggregate (composite)Chemical engineering

Abstract

fetched live from OpenAlex

Base course quality has a significant impact on the pavement load-bearing capacity and the layer strength could be improved using stabilization techniques. Asphalt emulsion is one of the commonly used materials for base course stabilization. Cement is usually added as an active filler to enhance the mix properties. Asphaltenes is a waste material derived from Alberta oil-sands Bitumen with no significant application in the industry. This study compares the impact of asphalt emulsion stabilized layers modified by asphaltenes and cement separately. For this purpose, asphaltenes and cement modified mixes with 1% and 2% by the mixes total weight were prepared. Both mixes mechanical properties were investigated through Marshall stability, indirect tensile strength (ITS), and tensile strength ratio (TSR). IDEAL-CT test was also used to evaluate the cracking resistance of the mixes. From the results, it was concluded that asphaltenes had a greater impact on increasing the Marshall stability, tensile strength, and cracking resistance of the mixes compared to cement. However, asphaltenes-modified samples were found to be more susceptible to moisture damages.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.058
GPT teacher head0.267
Teacher spread0.209 · 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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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