Vertical load-carrying behavior and design models for micropiles considering foundation configuration conditions
Bibliographic record
Abstract
In the present study, the vertical load-carrying behavior of micropile foundations with various configuration conditions was investigated based on results from model load tests. Considered configuration conditions included micropile inclination angle, spacing of micropiles, and types of micropile foundations. The ultimate load capacity of micropiles varied with installation angle, showing an initial increase and peak followed by a gradual decrease with further increase in installation angle. The ultimate load capacity of micropiled rafts was affected by both installation angle and micropile spacing. The load-carrying mechanism of micropiles for the inclined condition was proposed based on the decomposed axial and lateral load and resistance components. Using the proposed load-carrying mechanism and test results obtained in this study, the load capacity ratio for an inclined micropile was proposed. The group effect and interaction effect factors for group micropiles and micropiled rafts were proposed, respectively, all of which can be used to estimate the load-carrying capacity of micropile foundations. Field load tests were conducted and it was seen that estimated results using the proposed method were in good agreement with measured results. Additional comparison with case examples from the literature also confirmed the validity of the proposed method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".