Negative skin friction pile concepts with soil–structure interaction
Bibliographic record
Abstract
Code-based design of piles with negative skin friction (NSF) considers the NSF force (the drag force) as a load to be imposed on the pile as an unfavourable design action. These codes – for example Singapore Code of Practice CP4, UK Standard BS EN 8004:1986 and the recent Eurocode 7 (EC7) (BS EN 1997-1:2004) – would indirectly factor up the value of the drag force while at the same time disregard the shaft resistance above the neutral plane and factor down the positive shaft resistance below the neutral plane. Thus, the pile design in very deep soft clays typical of Singapore and Asian coastal plains will lead to very conservative pile lengths to meet the code requirements. The Fellenius unified pile design method recognised this deficiency, and it allows for better pile design with NSF taking into account the need for both force and settlement equilibrium between the pile and the soil. Fortunately, EC7 also allows for interactive pile–soil analysis using modern finite-element method tools that can optimise pile design for NSF, in particular when the remaining consolidation settlements around the piles are relatively small. This paper will compare these methods, provide insights into the proper understanding of NSF effects on pile behaviour and recommend the way forward for rational and economical pile design in settling soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".