Feasibility of Modelling of Soil-Structure Interaction Problems Using Full Three-Dimensional Finite Element Method
Bibliographic record
Abstract
Geotechnical problems have become more complex as we utilise every spare inch of the ground in urban environment. We need to improve our understanding of the ground behaviour in any underground works and to have more comprehensive, but accessible, techniques to analyse these problems. Ground movement prediction has become more important in such urban development and the need to incorporate advanced non-linear small strain constitutive model in complex soil-structure modelling has become the norm of the industry. Designers are now familiar with complex two-dimensional (2D) finite element (FE) analyses with highly complex soil models. However, three-dimensional (3D) analyses involving soil-structure interaction problems have been limited to large projects or projects involving fund-rich clients because of the high cost and long computation time involved in undertaking such analyses. Others have attempted such analyses by making many simplifications in order to minimise the size of the problems being investigated or even utilising technique involving the use of multi-processors in their computation. This paper presents some examples where full 3D modelling undertaken using a standard PC was used to solve routine geotechnical problems using a state-of-the-art non-linear small strain constitutive model.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".