A New Ensemble Prediction Method for Reclaimed Asphalt Pavement (RAP) Mixtures Containing Different Constituents
Bibliographic record
Abstract
Fatigue and rutting are two common damage types in asphalt pavements. Reclaimed asphalt pavement (RAP), as a sustainable approach in the pavement industry, deals with the foregoing damage. Fatigue and rutting characteristics of asphalt pavement are generally assessed using laboratory tests, taking a long time and consuming significant amounts of raw material. This study aims to propose a novel approach for predicting fatigue and rutting performance of RAP mixtures. A new ensemble prediction method, named COA-KNN, is introduced by combining the coyote optimization algorithm and K-nearest neighbor to increase the accuracy of fatigue and rutting prediction. In order to evaluate the accuracy, the proposed method was compared against robust prediction methods, including random forest (RF), gradient boosting (GB), decision tree regression (DT), and multiple linear regression (MLR). Afterward, the influence of each variable on the mentioned damages is examined, and the variables are ranked based on their relative influence on the mentioned damages. The results suggest that COA-KNN outperformed other prediction techniques when comparing different performance indicators. Total binder content in asphalt mixes and the PG span of the virgin binder added to the recycled asphalt mixture had the highest relative influence on fatigue and rutting performance, respectively.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".