Simulation of CPT Points and Empirical Settlement Using Simulated CPT Points with Kronecker-Product Gaussian Process Regression Approach
Bibliographic record
Abstract
In this paper, an application of data-driven Cone Penetration Test (CPT) simulations to computing empirical settlement is proposed. The paper utilizes three-dimensional (3D) random field generation of cone tip resistance (qc), sleeve friction (fs), and porewater pressure (u2). The parameters simulated are spatially variable, with correlation lengths simulated in the horizontal and vertical directions produced based on the probabilistic sampling. The simulation of these parameters is conditioned on known data within a geotechnical site. This is done by integrating a Gaussian-process-regression (GPR) trend model with a Kronecker-product sparse Bayesian learning (SBL) algorithm. The approach produces many simulated test values at unknown locations, and this data is aggregated to provide information about estimated settlement at that location using the empirical Schmertmann method. By structuring the 3D output space into field grid points, we can generate a simulated settlement profile over an area of the site. This novel approach allows engineers in practice to see fine-grained settlement details based on only a few CPT field tests, for which hundreds of tests would have necessarily been conducted before. An example is included which uses the outlined method to simulate settlement across many field grid points in a site, highlighting the method’s efficiency compared to layer construction based on average SBT values which is found in practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".