Interpretation of Cone Penetration Tests to Characterize Tropical Residual Soils Using Machine Learning
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Bibliographic record
Abstract
Cone penetration test (CPT) has been strongly applied to identify the soil profile and to provide some estimation of soil parameters.Several correlations exist, allowing the geo-characterization of the soil from CPT data.Such correlations must be carefully applied, and whenever possible, corrected with direct measurements of laboratory tests.Tropical residual soils have an inherent variability capable of providing very distinct results from very similar samples.Project designers must deal with this variability and correctly characterize these materials.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, distinguishing spatially 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.In addition, the groundwater level in the area is not clear, complicating the estimation of the vertical effective stress profile and further parameters from the CPT analysis.To overcome this issue, a sensitive analysis of the influence of groundwater level on the parameters of interest in this work (apparent overconsolidation ratio) was performed.To get as much information as possible from all datasets available, an Exploratory Data Analysis (EDA) followed by the application of an unsupervised learning algorithm was performed.Although an exactly spatial division from these soils were not possible, the EDA and unsupervised learning allow better visualization of the spatial distribution of these soils and grouping by desired characteristics, such as the pore pressure parameter.
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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 it