Artificial neural networks approach for swell pressure versus soil suction behaviour
Bibliographic record
Abstract
In this study, the swell pressure versus soil suction behaviour was investigated using artificial neural networks (ANNs). To achieve this, the results of the total suction measurements using thermocouple psychrometer technique and constant-volume swell tests in oedometers performed on statically compacted specimens of Bentonite–Kaolinite clay mixtures with varying soil properties were used. Two different ANN models have been developed to predict the total suction and swell pressure. The ANNs results were compared with the experimental values and found close to the experimental results. Moreover, several performance indices such as correlation coefficient, variance account for (VAF), and root mean square error (RMSE) were calculated to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. Therefore, it can be concluded that the initial soil suction is the most relevant state of suction that characterizes the potential swell pressures.
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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 | 0.001 |
| 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".