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Effects of Brick Powder on the Properties of Asphalt Mixes

2020· article· en· W3097578770 on OpenAlexaff
Fathi S. Almadwi, Gabriel J. Assaf

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsAsphaltRutMaterials scienceBrickDemolition wasteAsphalt pavementUltimate tensile strengthCreepComposite materialFiller (materials)Aggregate (composite)MoistureDemolitionGeotechnical engineeringCivil engineeringEngineering

Abstract

fetched live from OpenAlex

This study examines the use of brick powder (BP) reclaimed from construction and demolition waste as a filler in a hot-mix asphalt (HMA) in hot and desertic areas. For this research, two HMA mixtures were designed using different fillers: one using BP and the control using limestone powder (LSP). Readily available natural desert sand was also used in the mix. The samples were subjected to rutting, creep compliance, indirect tensile strength modulus (ITSM), and moisture susceptibility tests. The results were that the mixtures using BP had superior mechanical characteristics to mixtures using LSP. BP showed a marked improvement on asphalt pavement performance, and it resulted in a flexible road pavement with a long expected lifespan.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.198
Teacher spread0.181 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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