Estimation of the compaction parameters of aggregate base course using artificial neural networks
Bibliographic record
Abstract
Abstract The process of estimating the compaction parameters namely the maximum dry density (MDD) and optimum moisture content (OMC) through laboratory tests is time-consuming, labor-intensive, and costly. These issues can be avoided by developing prediction models that are able to accurately predict the compaction parameters from index properties that are easier to estimate in the laboratory. As a result, this study focuses on employing artificial neural networks (ANNs) for the prediction of the compaction parameters of aggregate base course samples from the grain size distribution and Atterberg limits. Additionally, different ANNs with different structures were tested in order to set the optimum hyperparameters that minimize the errors in the predictions. Specifically, this study investigates the impact of the number of hidden layers, number of neurons per hidden layer, and activation functions on the performance of the ANNs. Furthermore, the weight decay method, which is the most common regularization technique, was used during the training of the ANNs in order to avoid overfitting and control the changes in the connection weights. The results indicate that the optimum hyperparameter settings changes depending on the optimized output. Additionally, the ReLU activation function is the most stable function that produces the best predictions. Moreover, the results show that ANN approach represents a major innovative tool for accurately predicting the compaction parameters with R2values of 0.826 and 0.911 for predicting the MDD and OMC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".