Generation and evaluation of synthetic cone penetration test (CPT) data using various spatial interpolation techniques
Bibliographic record
Abstract
The cone penetration test (CPT) has been widely used in many geotechnical engineering applications, including soil identification and classification, and evaluation of different soil properties. However, the uncertainties associated with site variation are typical characteristics of subsurface soil conditions that cannot be ignored. Therefore, the effect of site variability on the correlated soil properties from collected field data, such as CPT data, obtained from discrete locations across the site needs to be evaluated. In this study, six well-established spatial interpolation techniques — ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) — were investigated to evaluate the best interpolation method for incorporating site variability. Six CPT test sites were used to evaluate the performance of these spatial interpretation methods. For each site, CPT profiles at specified locations were generated using the different interpolation techniques, and the generated CPT profiles were compared with the measured CPT profiles. The best-fit line of measured versus predicted cone tip resistance (qc) values, mean bias factor (λ), coefficient of variation (COV), and root mean square error (RMSE) were calculated for each CPT profile and used as a criteria for evaluating the different spatial interpolation methods. The results of this study demonstrated the ability of these spatial interpolation methods for generating CPT profiles with good accuracy. The slope of best-fit line of measured versus predicted qc ranges from 0.93 to 0.95, the mean of λ ranges from 0.90 to 0.98, and the COV ranges from 0.34 to 0.53. The IDW, OK, and SK showed the best spatial interpolation methods (in order) for four out of the six CPT sites. For site 4, OK, IDW, and SK showed the best spatial interpolation methods (in order); while for site 5, the three best spatial interpolation techniques are SK, IDW, and OK (in order).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".