Field investigation of lateral monotonic and cyclic performance of reinforced helical pulldown micropiles
Bibliographic record
Abstract
Different forms of grouted helical piles are increasingly used to support new and existing foundations. In particular, different methods are used to enhance the lateral and cyclic performance of helical piles for applications in seismic regions. This paper presents a field study on the lateral monotonic and cyclic behaviour of steel fibre–reinforced helical pulldown micropiles (RHPM) and fibre-reinforced polymer – steel fibre–reinforced helical pulldown micropiles (FRP–RHPM). The study shows that the grout shaft and (or) the fibre-reinforced polymer (FRP) sleeve significantly improve the helical pile lateral performance. In addition, the piles showed a significant ductility (no observed failure up to 75 mm displacement or 50% of pile diameter). Two-way cyclic loading resulted in overall degradation in pile response relative to its static performance. Degradation is found to stem from the formation of gaps between the pile and soil, rather than soil stiffness degradation. Formation of gaps leads to the piles having a “preferential direction” with one side providing higher resistance (i.e., stiffness) than the other side. Design charts of various pile configurations are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".