Numerical study on deformation characteristics of fibre-reinforced load-transfer platform and columns-supported embankments
Bibliographic record
Abstract
In this investigation, a ground-modification technique utilising a fibre-reinforced load-transfer platform (FRLTP) and columns-supported (CS) embankment constructed on multi-layered soft soils is proposed and investigated. After validating the proposed model with published data in the literature, numerical analysis was firstly conducted on the two-dimensional finite element model of a CS embankment without or with FRLTP to examine the influence of the FRLTP inclusion into the CS embankment system. Secondly, an extensive parametric study was performed to further investigate the effects of the FRLTP essential parameters — including platform thickness, shear strength, and tensile strength properties — and deformation modulus on the embankment performance during the construction and post-construction stages. Additionally, the influence of the embankment design parameters, such as column spacing, column length, and diameter, was examined. The numerical results reveal that the FRLTP inclusion can be effective in enhancing the CS embankment behaviour. It is also found that when increasing the platform thickness, the shear strength properties of FRLTP play a significant role in improving the overall performance of a column embankment with FRLTP.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".