A practical machine learning-based approach for predicting 1-D vertical swelling potential of expansive soils
Bibliographic record
Abstract
Several lightly loaded geotechnical and transportation infrastructures such as residential buildings, pipelines, roads, and railways have significant swelling potential challenges when they are placed on or within expansive soils. Reliable measurements of swelling potential of expansive soils are possible using conventional oedometer tests; however, their use in conventional practice is limited because they are time-consuming and costly. Several empirical equations have been proposed in the literature to alleviate these limitations; however, their applicability is limited for region-specific soils for which they have been developed. To overcome these limitations, in this study three machine learning-based prediction models were developed using a comprehensive global database of 173 expansive soils. The models, developed using Multivariate Adaptive Regression Splines and Multilayer Perceptron algorithms, show strong performance on the compiled dataset, with coefficients of determination (R 2 ) of 0.887 or higher. Among them is a simplified model expressed as an explicit equation that requires clay fraction, dry density, plasticity index, specific gravity, vertical load, and water content information that performs well with an R 2 of 0.964. Most importantly, the model provides reasonable estimations of several case studies from various regions of the world. In summary, the model serves as a reliable tool for estimating the in-situ swelling potential of expansive soils. Finally, this study results are promising for proposing heave mitigation strategies and to develop rational design procedures and maintenance measures for lightly loaded geotechnical and transportation infrastructure.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".