Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters
Bibliographic record
Abstract
Laboratory tests for the estimation of the compaction parameters, namely the maximum dry density (MDD) and optimum moisture content (OMC) are time-consuming and costly. Thus, this paper employs the artificial neural network technique for the prediction of the OMC and MDD for the aggregate base course from relatively easier index properties tests. The grain size distribution, plastic limit, and liquid limits are used as the inputs for the development of the ANNs. In this study, multiple ANNs (240 ANNs) are tested to choose the optimum ANN that produces the best predictions. This paper focuses on studying the impact of three different activation functions: number of hidden layers, number of neurons per hidden layer on the predictions, and heatmaps are generated to compare the performance of every ANN with different settings. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Additionally, the hyperbolic tangent activation is the most efficient activation function as it outperforms the other two activation functions. Additionally, the simplest ANN architectures results in the best predictions, as the performance of the ANNs deteriorates with the increase in the number of hidden layers or the number of neurons per hidden layers.
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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 | 0.002 |
| 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 | 0.000 |
| 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".