Field investigation of steel screw micropiles under axial loads in cohesionless and cohesive soils
Bibliographic record
Abstract
Steel screw micropiles are a new pile type for light load applications or building remediation, offering several advantages over conventional concrete piles. Unique feature of screw micropile shafts requires distinctive design approaches; despite the growing use, there is limited field measured data on their axial failure or torque-based design. This study aims to evaluate the ultimate capacity of screw micropiles, analyze the axial failure mode, develop empirical correlations between installation torque and ultimate capacity, and refine a torque estimation method based on Cone Penetration Tests (CPT). Full-scale axial compression tests in both cohesionless and cohesive soils were performed on five screw micropile types with diameters ranging from 76 mm to 114 mm and lengths from 1.6 m to 3.0 m. Each test was repeated three times, totalling 30 tests. In-situ and laboratory investigations were conducted to characterize the soils. Results showed that in cohesionless soil, installation torque increased linearly with depth; while in cohesive soil, torque tended to stabilize after the threaded segment was fully embedded. The evidence suggests the impact of soil strength and disturbance on installation torque. A reliable linear relationship was observed between installation torque and ultimate capacities, with torque factors (defined as the ratio of pile ultimate capacity to max installation torque) ranging from 21.5 to 27.8 m −1 . Back-analysis suggested that the axial failure is governed by local bearing beneath each thread. The CPT-based torque estimation method in previous studies for piles in cohesive soil was revised to include the effect of smooth segment, and the revised method suggested consistent comparison with the measured torque.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".