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Record W2142163781 · doi:10.5267/j.esm.2015.2.002

Study of loading waveform, loading duration, rest period and stress level on fatigue life of asphalt mixtures

2015· article· en· W2142163781 on OpenAlexvenueno aff
Ali Mansourkhaki, Alireza Samdzadeh, Majid Jebalbarezi

Bibliographic record

VenueEngineering Solid Mechanics · 2015
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltWaveformDuration (music)Materials scienceRest (music)Structural engineeringStress (linguistics)Period (music)Composite materialEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

The main objective of this paper is to study the effects of haversinse and triangular loading waveforms on the fatigue life of Hot Mix Asphalt (HMA) specimens. Effects of load duration, rest period and stress level are also studied for the asphalt mixtures at 25oC. An indirect tensile test with strain control was performed to determine the fatigue life of asphalt. The fatigue tests were performed at two stress levels (170 and 250 kPa), two waveforms (haversine and triangle), three load duration (100, 200, 400 ms), and two rest period to load duration ratios (4 and 9). The obtained results showed that fatigue life of haversine waveform is less than fatigue life of triangle waveform. As the area under the loading curve is increased (stress level is increased or deformed), effect of rest period on the fatigue life decreases. On the other hand, as the tire contact area is increased, the induced tire pressure reduction decreases its destructive effects on the asphalt layer. As the load duration is decreased, fatigue life will increase. This effect is more pronounced for lower stress levels than the higher stress levels.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.059
GPT teacher head0.280
Teacher spread0.221 · 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

Citations21
Published2015
Admission routes1
Has abstractyes

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