Dynamic complex modulus predictions of hot-mix asphalt using a micromechanical-based finite element model
Bibliographic record
Abstract
A micromechanical-based finite element (FE) model was used to predict the dynamic complex modulus ( E*) of the hot-mix asphalt (HMA). The microstructure of HMA was captured with a high resolution scanner. Two material phases (aggregates and sand mastic) of HMA were modelled with finite elements. The sand mastic herein was composed of fines and asphalt binder with some fine aggregates. The dynamic complex modulus of the sand mastic under different temperatures and loading frequencies was measured in an experimental program. The corresponding principles were applied to bridge the elastic simulation and viscoelastic behavior with the input of the viscoelastic mastic properties. The input parameters in the FE model include the dynamic complex modulus of the sand mastic, the elastic modulus of the aggregates, and the microstructure of the HMA. The E* values of the HMA were measured and used to compare the E* predicted from the FE model. It is found that the FE approach used in this paper has the ability to predict HMA dynamic modulus across a range of temperatures and loading frequencies. The FE prediction of the E* was compared with a recently developed discrete element modelling approach and found the E* prediction from these two approaches to be very similar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".