Identifying Soft Soils using Pore-Pressure Parameters: A Machine Learning Approach
Bibliographic record
Abstract
The cone penetration test (CPT) is a widely used method for identifying soil profiles and estimating soil parameters.Numerous correlations have been established to facilitate geo-characterization of soils based on CPT data.However, caution must be exercised when applying these correlations and laboratory tests should be used to validate them.Tropical residual soils are highly variable, even for seemingly similar samples, which can make it difficult for project designers to accurately characterize them.The present work focuses on a case study where the goal was to distinguish and characterize two soft soils existent on the foundation of a tailings dam in the southwest of Brazil.The construction of the dam is still ongoing, and its foundation belongs to a complex geological environment with soft soils that can reach NSPT blows as low as its own weight.The geological survey identifies two horizons of residual soil of dolomitic phyllite: soft and very soft.However, spatially distinguishing this material regarding its consistence has shown to be a challenging task.Since they differ essentially on the degree of weathering, most parameters for both materials are quite similar, and from laboratory tests, the parameter that helps differentiate these soils is the pore pressure Skempton parameter at failure -Af.Based on these findings, it can be inferred that the pore-pressure parameter Bq in CPT represents the excess pore-pressure during the test, whereas Af describes the excess pore-pressure at failure during triaxial tests.Despite the lack of a currently established theoretical correlation between the two parameters, they can offer valuable insight into the soil's response to rapid loading.Notably, both measures have proven to be effective in distinguishing between residual soils, even though they are distinct measures.In this study, the Bq and Af parameters are employed to classify soils using an unsupervised learning method, specifically the K-means algorithm.The resulting clusters exhibit strong agreement with borehole profiles near the CPT locations.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".