Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT
Bibliographic record
Abstract
Liquefaction is a hazardous seismic-based phenomenon, which causes an abrupt decrease in soil strength properties and can result in the massive destruction of the built environment. This research presents a novel approach to reduce the risk of soil liquefaction using jet-grouted micropiles in clean sands. The saturated soil profile of the study project mainly contains clean sands, which are suitable to more reliably employ simplified soil liquefaction analyses. The grouting is conducted using 420 micropiles to increase the existing soil properties. The effect of jet grouting on reducing the potential of liquefaction is assessed using the results of the cone penetration test (CPT) and the standard penetration test (SPT), which were conducted before and after jet grouting by implementing micropiles in the project sites. According to three CPT-based liquefaction analyses, the Juang method predicts the most effective improvement range of the factor of safety in the clean sand. The Boulanger and Idriss, and Eurocode methods show comparable evaluations. Results of the SPT-based analyses show the most considerable increase of the factor of safety following the Boulanger and Idriss, and NCEER approaches in the SP soil. CPT- and SPT-based analyses confirm the effectiveness of jet grouting by micropiles on enhancing soil properties and reducing the risk of liquefaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".