How distribution characteristics of a soil property affect probabilistic foundation settlement — from the aspect of the first four statistical moments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effects of the first four statistical moments defining the statistical characteristic of elastic modulus on the probabilistic foundation settlement are investigated in this study. By combining the Hermite probability model and spectral representation method, a method to simulate nonGaussian homogenous fields based on the first four statistical moments is proposed. Linear elastic finite element models are employed to study the total settlement and the differential settlement of a shallow foundation. Probabilistic measurements of total–differential settlement obtained by the Monte Carlo simulations are presented. For the cases considered, the effects of skewness and kurtosis defining the probabilistic characteristic of elastic modulus on the total–differential settlement of a probabilistic foundation are illustrated. The computed results show that the value of skewness has a more significant effect on the probabilistic foundation settlement than kurtosis, and the case with the smallest skewness is observed as the most critical one.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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