Bibliographic record
Abstract
The ground is a complex engineering material and how to characterize it realistically is a very difficult problem. It is well known that the engineering properties of the ground can vary quite dramatically from point to point throughout a site, and even more so from site to site, and that these properties are highly uncertain. It is also well known that the ground, when subjected to an imposed or self-load, will fail along its weakest path, however tortuous that might be. Given the complexity of the ground, it makes sense to characterize the ground using models which allow for its quite uncertain spatial variability. It also makes sense to use response prediction models which take both spatial variability in ground properties and the tendency of failure to follow weakest paths through the ground into account. The Random Finite Element Method (RFEM) combines spatially varying random field ground models with the finite element method to yield a reliability-based geotechnical methodology which accounts for both spatial variability and weakest path failure mechanisms. Besides being able to realistically model spatial variability in ground properties along with being able to follow the weakest path through the soil, mass, RFEM also provides the significant advantage of being able to account for site understanding in the design process. This paper describes the Random Finite Element Method along with a few of its significant results over a variety of common geotechnical problems. The latter include ground-water modeling, shallow foundation settlement and bearing capacity, deep foundation capacity, and slope stability. LRFD code development will be discussed along the way.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".