Fatigue damage analysis in asphalt concrete mixtures using the dissipated energy approach
Bibliographic record
Abstract
An asphalt concrete damage–energy fatigue approach based on the concept of change in dissipated energy is presented in this paper. The damage–energy based fatigue approach is simple and based on a sound theoretical background. The central concept of the energy approach is the energy fatigue curve, which is based on two key elements, namely the plateau value (PV) and the number of load cycles to true failure (Ntf). The plateau value represents the constant value of the percentage of dissipated energy that produces damage to the material under cyclic loading. Failure is defined as the number of load cycles at which this percentage of dissipated energy begins to increase rapidly, indicating instability. Flexural fatigue testing was used to test hundreds of asphalt concrete beams, mainly under controlled-strain testing conditions. It was found that PV is highly dependent on the initial loading conditions, stress, strain, and dissipated energy. As a result, it can be used conveniently in pavement design. The number of load cycles to 50% reduction in initial stiffness was found to be highly correlated with the new failure point (Ntf). Using the dissipated energy concepts in fatigue analysis makes it possible to account for damage accumulation in a straightforward manner.Key words: fatigue of asphalt concrete, dissipated energy, damage, energy ratio.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".