Integrated predictive artificial neural network fatigue endurance limit model for asphalt concrete pavements
Bibliographic record
Abstract
Asphalt endurance limit is a strain value if experienced by asphalt pavement layer, no accumulated damage will occur and is directly related to asphalt healing. Therefore, if the pavement experiences this value of strain, or lower, no fatigue damage would accumulate within that pavement section. Beam fatigue test data conducted under the NCHRP Project 9-44A were extracted and utilized to create an artificial neural network predictive model (ANN) to determine the endurance limit strain values for conventional asphalt concrete pavements. The developed ANN model architecture as well as how to utilize it to predict the endurance limit were demonstrated and discussed in detail. Also, a stand-alone equation that is capable in the prediction of the endurance limit strain value, separate from the ANN model environment, was derived utilizing the eclectic extraction approach. The model training and validation data included 934 beam fatigue laboratory data points, as extracted from NCHRP Project 9-44A report. The developed model was able to determine the endurance limit strain value as a function of the stiffness ratio, number of cycles to failure, initial stiffness and rest period, and had a reasonable coefficient of determination (R 2 ) value, which indicates the reliability of both the developed ANN model and the stand-alone equation. Furthermore, a correlation between the endurance limit strain values, as predicted utilizing the generated regression model under the NCHRP project 944-A, and the endurance limit strain values predicted utilizing the stand-alone ANN derived equation was found with a high R 2 value.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".