Integrating finite element and load-transfer analyses in modelling the effects of dewatering on pile settlement behaviour
Bibliographic record
Abstract
A method of analysis is developed by integrating finite element (FE) and load-transfer analyses to predict the negative shaft resistance and settlement of piles due to ground water lowering. A program is written in MATLAB for linking the FE results of ground movements with the input interface system of a new pile load–settlement analysis program (named “PILESET”) developed by the author. PILESET is specially designed to allow automatic input of electronic site investigation data, although manual input of laboratory soil test data is also possible. Using PILESET, custom-defined load-transfer relationships can be either input manually or calculated internally by PILESET based on the input data from in situ or laboratory soil tests. To demonstrate the validity of the suggested analysis procedure, a case record is analyzed where sump pumping was to be carried out underneath a deep basement situated close to an existing building supported on 15 m long piles. Based on assumed steady-state flow conditions, ground settlements are calculated using FE analysis and used with site investigation data to predict the negative shaft resistance and settlement induced in the piles. The results are found to agree well with field measurements.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".