A novel short pile foundation system bonded to highly cemented layers for settlement control
Bibliographic record
Abstract
While design methods of deep foundations are mainly developed for homogenous soil deposits, the presence of highly cemented layers could lead to underestimation of resistance and overestimation of settlement of pile foundations. This study presents a novel approach using competent caliche layers bonded to the top and bottom of a continuous flight auger (CFA) pile as a new composite foundation system named caliche stiffened pile (CSP). The key objective is to optimize the required pile length in a cost-effective approach without ameliorating soil properties. Settlements of the CSP foundation for a high-rise building were monitored and full-scale tests were conducted to measure piles’ capacity. Finite element back analyses were performed to avoid adverse effect of sample disturbance in settlement calculations. A back calculation of a test fill embankment was performed to determine soil stiffness parameters by simulating an unscheduled imposed load to the structure. Impacts of the CSP on controlling the settlement of pile foundation and optimizing the required pile length are investigated using finite element analysis and a parametric study. The proposed CSP foundation can reduce the CFA pile settlement significantly in the presence of caliche layers with thickness equal or greater than a pile diameter at CFA pile head and toe, where the CSP is located.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".