A Framework for Estimating Matric Suction in Compacted Fine‐Grained Soils Based on a Machine Learning‐Assisted Conceptual Model
Bibliographic record
Abstract
ABSTRACT In this study, a hybrid black‐ and white‐box machine learning (ML) framework is proposed for matric suction estimation in compacted fine‐grained soils, utilizing ML techniques, namely, particle swarm optimized support vector regression (PSO‐SVR) and multi‐gene genetic programming (MGGP). This objective is achieved through developing a novel ML‐based method for designing requisite soil parameters, including a new parameter, the “effective degree of aggregation”. This parameter captures the influence of varying soil structures associated with different initial water content conditions in compacted fine‐grained soils for estimating matric suction. Additionally, sensitivity analyses are performed to better understand the significance of the effective degree of aggregation and other critical soil properties. Explicit equations are derived from the MGGP models, enabling their use for matric suction estimation using spreadsheets and alleviating the reliance on complex programming tools. The proposed models are promising for use in the prediction of hydro‐mechanical behavior and other related compacted fine‐grained soil properties, facilitating their application in conventional geotechnical engineering practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".