Automatic soil identification from penetrometric signal by using artificial intelligence techniques
Bibliographic record
Abstract
Conventional geotechnical soil classifications aim to classify soils into families with geotechnical characteristics and therefore similar behaviour; however, they require core samples and laboratory identification testing. Several empirical systems for estimating the nature of the soils have been developed on the basis of several in situ geotechnical tests. However, at present these systems remain empirical and they are often only used on an indicative basis. The objective of this article is, based on the analysis of dynamic penetrometric signals, to develop a methodology able to provide an estimate of the nature of the soil crossed. The methodology developed provide an automatic classification based on artificial neuron networks (ANNs) tools. Two types of ANN architectures were considered: multi-layer feedforward perceptron (MFP) and probabilistic neural network (PNN). The learning of these two tools was achieved through a base carried out in the laboratory and in situ. Both classification models were then tested in blind conditions and showed a good efficiency for calibrated soils and promising results for in situ soils.
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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 | 0.001 |
| 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) | 0.004 | 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".