Predicting the Resilient Modulus of Unbound Granular Materials by Neural Networks
Bibliographic record
Abstract
The process of pavement design requires the provision of material properties. For mechanistic'empirical design methods, the resilient modulus represents the most suitable alternative for describing the behavior of aggregate materials commonly used in sub-base and base layers. However, the adoption of the resilient modulus has been slow due to the complicated nature of the laboratory test used to obtain the parameter and its cost. Attempts to correlate the resilient modulus to the widely used California Bearing Ratio and otherempirical parameters in the past fall short of providing reasonably accurate estimates of the parameter. With the renewed interest in using the resilient modulus as advocated by the AASHTO 2002 Guide, a quick and inexpensive solution to providing accurate estimates of this parameter is needed. This paper presents the artificial neural network (ANN) technique as a promising method that can help designers have a good first-step estimation of the resilient modulus based on data accumulated over the years. The study highlights the use of the ANN technique, which utilizes simple parameters as input to predict the resilient modulus ofunbound granular materials. Results of ANN simulations confirm the potential of the technique to predict the resilient modulus of compacted samples tested at various compaction densities, states of stress and moisture contents. Such a tool represents an attractive alternative to laboratory testing for small jurisdictions with limited budgets and personnel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".