Bibliographic record
Abstract
Helical piles have often been characterized as handy little tools in the toolbox of solutions for structural support of foundations. They have provided options to construct foundations and anchorage in very challenging locations where more common methods were impractical. The stigma of being efficient for lightly loaded foundations has limited their use as highly loaded deep foundations. This paper will dispel the notion that helical piles are not a deep foundation option for large structures by presenting the case study of a 32-storey building in London, Ontario supported on helical piles. The building was constructed in a downtown location where all the problems associated with deep foundations were present – high water table, vibrations, compressible soil layers, limited access, logistics of soil removal, seismic considerations. These issues all had costs associated with them that helical piles and their installation process would limit or eliminate. This paper will present the detailed solution that helical piles offered the constructor of the new building. The solution included pre-design load testing and analysis of the results. The advantages of using helical piles in this case will also be presented from technical and constructability viewpoints to demonstrate why they should be considered for large structures.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".