Predicting the compression index of expansive soils with hybrid machine learning approaches
Bibliographic record
Abstract
Expansive soils pose significant challenges for geo-infrastructure design, construction, and maintenance because of their moisture-induced volume changes. Despite extensive research on swelling behavior, the compression index ( C c ), an important indicator of soil compressibility has received relatively limited attention. C c is conventionally obtained from time-consuming and costly consolidation tests, while empirical equations derived from general clays may not provide reliable estimates for expansive soils. To address this gap, seven machine learning models based on five algorithms were developed to estimate the C c of expansive soils using soil properties readily obtained from conventional laboratory tests. A comprehensive dataset comprising 238 expansive soil samples, compiled from 60 years of published literature across various regions of the world, is employed to train and validate the proposed models. Among them, Model 2 based on Newton-Raphson-Based Optimizer optimized Extreme Gradient Boosting (NRBO-XGBoost) achieved the best performance, with a coefficient of determination ( R 2 ) of 0.903 and a root mean square error (RMSE) of 0.029 on the test set. Sensitivity analysis showed that the plasticity index was the most influential factor (31.1 %), followed by liquid limit (29.4 %), initial void ratio (25.3 %), and dry density (14.1 %), highlighting its primary influence on soil compressibility. Additionally, a simplified equation derived from the Multilayer Perceptron (MLP) designated as Model 4 is validated through three case studies. The settlement predictions deviated by 5–14 % from field measurements, offering a practical tool without the need for machine learning techniques. The findings provide useful guidance for developing rational design strategies for geo-infrastructures affected by expansive soils.
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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 | 0.001 |
| 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".