A Probabilistic Predictive Model for Foundation Settlement on Liquefiable Soils Improved with Ground Densification
Bibliographic record
Abstract
In this paper, we present a probabilistic predictive procedure for a foundation’s permanent average settlement on liquefiable soils improved with ground densification. The proposed procedure is based on 770 three-dimensional (3D), fully coupled, effective-stress, finite-element analyses designed through quasi-Monte Carlo sampling of key input parameters. The numerical models are themselves calibrated and validated with centrifuge model studies, and they consider realistic, nonlinear, 3D structures on shallow foundations, seismic soil–structure interaction, interlayering and layer cross interactions, ground densification properties and geometry, and ground motion characteristics. We use nonlinear regression with lasso-type regularization to estimate model coefficients. The primary predictors of a foundation’s settlement are identified as the cumulative absolute velocity of the outcropping rock motion; total thickness of the soil deposit above bedrock and cumulative thickness of the critical liquefiable layer(s); the foundation’s bearing pressure, size, and embedment depth; the structure’s total height; the achieved density and size of ground improvement; and the thickness of the remaining undensified susceptible soils within the foundation’s influence zone. In the end, the predictive model is shown to capture the trends in a limited number of centrifuge and field case histories collected from the literature. The insight from the numerical database and the first-of-its-kind predictive model aims to guide the design of liquefaction mitigation strategies that improve the performance of the soil–foundation–structure system holistically and reliably.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".