Centrifuge modeling on effectiveness of liquefaction mitigation measures for clayey sand ground under large dams
Bibliographic record
Abstract
Seismic loading has been widely recognized as a critical factor contributing to soil liquefaction. This paper introduces centrifuge tests conducted to characterize the seismic response of clayey sand foundations under surcharge induced by upper structures such as dams, with a focus on examining the effectiveness of different liquefaction mitigation measures. Three models with a surcharged block above are considered: one with soil untreated, one improved with stone columns, and the other enhanced by closed diaphragm walls. In addition, a free ground model is tested to examine the dynamic characteristics of the tested soil. Results show that the soil used is prone to liquefaction, but this trend can be somewhat suppressed by the presence of the surcharge. However, the excess pore pressure within the shallow layer keeps rising after shaking, posing the surcharged structure to instability. With the inclusion of stone columns, seepage can be effectively facilitated, thus eliminate the large pore pressure concentration. The construction of closed diaphragm walls effectively reduces the surface settlement by providing lateral restraint of the soil core. This investigation sheds light on the liquefaction mitigation mechanisms of different measures for clayey sand subjected to large overburden and provides references for improving the seismic design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".