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Record W2001063821 · doi:10.3141/2155-06

Performance of Recycled Hot-Mix Asphalt Overlays in Rehabilitation of Flexible Pavements

2010· article· en· W2001063821 on OpenAlexaboutno aff
Regis Carvalho, H R Ghafarian Shirazi, Manuel Ayres, Olga Selezneva

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2010
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersFederal Highway Administration
KeywordsOverlayAsphalt pavementAsphaltEngineeringReuseRutAggregate (composite)Statistical analysisForensic engineeringCivil engineeringEnvironmental scienceWaste managementComputer scienceMathematicsStatisticsMaterials science

Abstract

fetched live from OpenAlex

The most frequent application of recycling materials in pavements is the reuse of reclaimed asphalt pavement (RAP) to produce recycled hot-mix asphalt (HMA). When designed properly, RAP mixes have demonstrated quality comparable to virgin HMAs in laboratory tests. Despite all the information available about the quality of RAP mixes, obstacles still promote their more frequent use in pavement engineering. Short- and long-term field performance of RAP mixes was investigated compared with virgin HMA overlays used in flexible pavements. Data from the 18 Specific Pavement Studies-5 (SPS-5) sites from the Long-Term Pavement Performance program located across the United States and Canada were used. Performance data were collected during periods ranging from 8 to 17 years. Repeated measures analysis of variance was the statistical analysis tool chosen, pairing distress measurements with survey dates to compare performance and response. The results suggest that in the majority of scenarios RAP mixes have performance statistically equivalent to virgin HMA mixes. The statistical equivalency of deflections suggests that RAP overlays can provide structural improvement equivalent to virgin HMA overlays.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.050
GPT teacher head0.356
Teacher spread0.305 · 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 designObservational
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

Citations18
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207