Model‐Free Data‐Driven Computational Analysis for Soil Consolidation Problems
Bibliographic record
Abstract
ABSTRACT Soils are inherently uncertain natural materials. In geotechnical engineering, soil properties are fundamentally characterised by testing small samples. The results will then be utilised to determine appropriate geomaterial constitutive models and associated parameters for implementation in conventional computational procedures such as finite element analysis (FEM). However, the accuracy and generalisation capability of such analyses largely depends on the selection of models, which may vary according to the specific applications. To overcome these limitations, computational approaches that do not rely on predefined soil constitutive models are emerging. In this paper, the formulation of data‐driven computing for fluid transport in porous media with particular reference to soil consolidation is derived. This does not rely on any constitutive flow law or models; instead, it directly uses the experimental data on fluid transport properties to compute fluid phase distribution during transient changes of the porous skeleton. For a discretised domain, the data‐driven solver assigns each element or quadrature point a state from an experimental dataset, satisfying mass conservation condition and fluid pressure gradient definition simultaneously. By introducing a penalty function defined by the quadratic distance between local state and material state, the problem is formulated as a constrained minimisation task solved explicitly by the Lagrange multipliers method. Subsequently, several cases were analysed using the proposed data‐driven method and compared with analytical and finite element (FE) solutions. In these tests, the data‐driven method shows good accuracy and convergence properties with further discussion on the influence of the scale and noise level of the dataset.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".