Estimation of the installation torque–capacity correlation of helical pile considering spatially variable clays
Bibliographic record
Abstract
In the offshore fields, helical piles are increasingly deemed to constitute suitable tools for anchoring floating structures and wind turbines. A large number of studies have been published to explore the installation torque–capacity correlation, and most of them are conducted in a deterministic manner. However, natural soils are inherently spatially varying, and analyses taking such variation into account might be closer to the reality. To address this issue, this paper examines the installation and uplift process of helical piles considering spatially varying soils via three-dimensional large deformation random finite element analyses within a Monte Carlo framework. Computed values of the installation torques and the uplift capacities compare well with the results in existing publications, therefore verifying the applicability of the numerical model. Spatially varying soil strength is mapped through the random field, followed by Monte Carlo simulations conducted to determine the torque–capacity correlation in random soils. The results suggest that the torque–capacity correlation might be misestimated once the spatially random soil properties are overlooked. Besides, probabilistic assessments of the pile torque–capacity correlation are performed, which may be of great interest to engineering practitioners in the design method of the helical pile.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".