Experimental Investigation and Prediction of the Permanent Deformation of Crushed Waste Rock Using an Artificial Neural Network Model
Bibliographic record
Abstract
The gradual accumulation of permanent deformation in unbound granular material layers is one of the main reasons for flexible pavement rutting. The accurate determination of permanent deformation behavior in pavement materials is critical for the successful design of pavement systems. However, predicting the permanent deformation is complex, and the available empirical regression models have limited accuracy and applicability. In this study, multistage repeated load triaxial tests were carried out under different stress levels in order to evaluate the permanent strain and shakedown ranges of crushed waste rock. The plastic shakedown limit and plastic creep limit were determined so as to estimate the shakedown range of crushed waste rock under certain stress conditions. The Rahman and Erlingsson model (extended using a time-hardening approach) performed better than other models at fitting the accumulated permanent strains (R2 > 0.92), although the prediction accuracy of the shakedown range was relatively low (<85%). An artificial neural network (ANN) model was therefore developed, based on the experimental results, to predict the permanent strain of crushed waste rock. The ANN model consisted of three hidden layers (50 neurons per layer) with Tanh activation function and could predict the permanent strain (R2 > 0.97) and shakedown ranges (accuracy >93%) satisfactorily.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".