Rational relationship between the fatigue curves of asphalt mixes obtained from tension/compression and 4-point bending tests
Bibliographic record
Abstract
The fatigue properties of asphalt mixes are usually established according to a series of laboratory cyclic loading tests at fixed temperature (θ) and frequency (f). Unfortunately, the results are different when considering different types of tests. This paper proposes a rational method to link the 4-point bending (4PB) and tension/compression (TC) fatigue test results. First a theoretical analysis of the 4PB fatigue test is presented. This analysis allows to introduce a link between the fatigue curves (modulus or damage versus number of cycles) of this type of test with the results from uniform TC tests. The 4PB fatigue curve is superimposed with the TC fatigue curve if the reference strain amplitude is correctly chosen. This amplitude must not be the maximum strain at the boundary fibre of the beam (ε0max), as usually considered, but a lower proposed value (ε0h0). Then, the previous approach is extended to the fatigue failure Wöhler’s law, which is a line in the Log–Log plot of the number of cycles at failure versus the loading amplitude (stress or strain). An experimental campaign on a Canadian asphalt concrete (HMAC) validates the developped approach. It is an important output from this research as fatigue life from one type of test can be obtained from the other type of test in the case of strain (or displacement) control tests.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".