Estimating the effectiveness of stone columns in mitigating post-liquefaction settlement using Plaxis 2D
Bibliographic record
Abstract
When the excess pore water pressure generated during an earthquake dissipates in saturated loose sand, it causes post-liquefaction reconsolidation that can potentially yield substantial damage to the structure. To build resilient infrastructure, it is paramount to estimate these settlements as well as introduce soil reinforcement techniques to mitigate associated risks. Although there are abundant studies on liquefaction triggering assessment, the study of post-liquefaction settlement and the effects of stone columns as soil reinforcement is a relatively less established field. Generally, simplified empirical methods are employed for settlement evaluations. However, they possess several limitations such as the influence of non-liquefiable layers, soil fabric, permeability, and so on. Numerical models can be utilized to capture these effects with proper validation. This study evaluates the performance of stone columns in reducing seismically induced post-liquefaction settlement utilizing the Finite Element Method (FEM) and constitutive relationship, PM4Sand model, as it has been extended to account for reconsolidation settlement. The ability of the numerical framework to capture reconsolidation settlement is validated by replicating a shake table test performed on Ottawa F-55 sand. Results are compared with a previous numerical study inspired by the same experiment. After validation, a generic numerical model is proposed, and the performance of the natural ground and the reinforced ground is compared. A parametric analysis using 12 different ground motions is performed to assess the effect of varying ground motion intensity on the post-liquefaction settlement. The analysis is also performed with the conventional PM4Sand model (without the extension for reconsolidation). Finally, simulations are performed with a footing load above the soil model. The results demonstrate that (a) the presence of stone columns reduces post-liquefaction settlement, and (b) conventional constitutive models can highly underpredict post-liquefaction settlement. Further research is required to assess the effects of (a) 3D, (b) variations in permeability, (c) parametric analysis of stone columns, and (d) densification of stone columns.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".