Selection of standard penetration test number for geotechnical investigation of a vertical cross section considering spatial variability and correlation in soil properties
Bibliographic record
Abstract
The standard penetration test (SPT) is a widely used in situ test for characterizing variation of subsurface soil properties, and results of site investigation are usually simplified as a two-dimensional (2D) vertical cross section for subsequent geotechnical design and construction. Current geotechnical design codes and guidelines only provide general recommendations for selection of an appropriate number of in situ tests (e.g., SPT) (e.g., the greater variability of subsurface conditions, the larger number of SPTs required to obtain sufficient underground information). No quantitative or rational method is available for selecting the appropriate number of SPTs considering spatial variability and correlation in subsurface conditions. A comprehensive parametric study is carried out in this study to investigate the influence of spatial variability in subsurface conditions on the minimum SPT number needed for satisfying an accuracy requirement of site investigation. Random field is adopted to model spatial variation and correlation in soil properties in typical site conditions, and 2D Bayesian compressive sampling is used to interpolate sparse SPT data. Based on the parametric study results, a statistical chart is developed for geotechnical engineers to conveniently select an appropriate number of SPTs in a vertical cross section. Real SPT data from New Zealand are used to illustrate and validate the proposed method.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".