Seismic performance of helical piles in dry sand from large-scale shaking table tests
Bibliographic record
Abstract
Recent earthquakes have highlighted the need for safe and efficient construction of earthquake-resilient structures. Meanwhile, helical piles are gaining popularity as a foundation not only for new construction, but also retrofitted structures, owing to their advantages over conventional driven pile alternatives. In addition, several structures supported by helical piles in New Zealand sustained minimal damage during the Christchurch earthquake, proving that helical piles can be a suitable foundation option in highly seismic areas. However, there is a knowledge gap concerning their seismic performance. Therefore, the current study investigates the performance of helical piles through a full-scale shaking table test programme – the inaugural large-scale seismic testing programme performed on helical piles. The experimental set-up involved installing ten steel piles with different configurations and pile head masses in dry sand enclosed in a laminar shear box mounted on the Network for Earthquake Engineering Simulation at University of California, San Diego (NEES/UCSD) large high-performance outdoor shaking table (LHPOST). The loading scheme consisted of white noise and two earthquake time histories with varying intensity and frequency content. The experimental results are presented in terms of the natural frequency and response of test piles (peak deflections). The effects of loading frequency and intensity and the pile's geometrical configuration and installation method were evaluated.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".