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Record W588995495

Three Years of Dynamic Modulus Testing of Asphalt Mixes

2009· article· en· W588995495 on OpenAlexaboutno aff
Márta Juhász, C McMillan, Robert J. Kohlenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltDynamic modulusStiffnessAsphalt pavementAnticipation (artificial intelligence)Asphalt concreteEngineeringStructural engineeringGeotechnical engineeringCivil engineeringComputer scienceMaterials scienceComposite materialMachine learningDynamic mechanical analysis
DOInot available

Abstract

fetched live from OpenAlex

The dynamic modulus (E*) of an asphalt mix characterizes its stiffness response under sinusoidal loading. E* is a key input parameter into the Mechanistic-Empirical Pavement Design Guide (MEPDG) and is a required parameter for a Level 1 design. It is also a parameter with which most practitioners have little experience. In anticipation of the future implementation of the MEPDG, in 2005, Alberta Transportation established an annual E* testing program of select asphalt concrete pavement mixtures. The intent of this testing was to develop some background on the range of values that might be expected and to confirm the ability of E* to differentiate between different mix types. This paper documents the testing that Alberta Transportation has undertaken to date, including some trials with only duplicate samples, as well as some trials using four-inch thick samples. The results of the E* testing, the E* master curves, and a comparison of the results to those obtained through the Witczak predictive equation are also presented. (A)

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.263

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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 designSimulation or modeling
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
Published2009
Admission routes1
Has abstractyes

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