Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation
Bibliographic record
Abstract
Random field theory has been increasingly used in probabilistic geotechnical analyses over the past few decades, where a random field generator with random field parameters is needed to simulate random field samples (RFSs) of interest. Estimation of random field parameters, particularly correlation functions or correlation length, generally requires extensive measurements. However, the data gathered from site characterizations are usually sparse, particularly for small or medium sized projects. Therefore, it is difficult to provide an accurate estimation on random field parameters, and the random field parameters estimated and subsequently used in RFS generation might contain significant uncertainty. This leads to a challenge of properly simulating RFSs in consideration of such uncertainty. This paper aims to address this challenge by developing a novel random field generator, which is capable of directly generating RFSs from sparse measurements obtained during site characterization and properly accounting for uncertainty associated with interpretation of sparse data. The proposed generator is based on Bayesian compressive sampling (BCS) and Karhunen–Loève (KL) expansion, and it is denoted as BCS–KL generator. The proposed BCS–KL generator is illustrated and validated through both simulated data and 30 sets of cone penetration test data measured throughout the world.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".