Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing
Bibliographic record
Abstract
The subgrade resilient modulus is an important parameter in pavement analysis and design. However, available non-destructive testing devices such as the falling weight deflectometer (FWD) have limitations that prevent their widespread use at the network level. This study describes the development of a model that utilizes the rolling wheel deflectometer (RWD) measurements to predict the subgrade resilient modulus at the network level for flexible pavements. Measurements of RWD and FWD obtained from a testing program conducted in Louisiana were used to train an artificial neural network (ANN) based model. The ANN model was validated using data from a testing program independently conducted in Minnesota. The ANN model showed acceptable accuracy in both the development and validation phases with coefficients of determination of 0.73 and 0.72, respectively. Furthermore, the limits of agreement methodology showed that 95% of the differences between the subgrade resilient modulus calculated based on FWD and RWD measurements will not exceed the range of ±21 MPa (±3 ksi).
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".