Modeling axial strain-deviatoric stress response of frozen sands with enhanced LSTM approach
Bibliographic record
Abstract
Data-driven approaches hold promise for modeling the highly nonlinear stress-strain behavior of frozen soils in comparison to standalone machine learning models which often overfit and lack generalizability. To address these issues, in this study an enhanced fundamental Long Short-Term Memory (LSTM) model with an iterative EXtreme Gradient Boosting (XGBoost) algorithm is proposed to predict the axial strain-deviatoric stress relationship of frozen sands. Using degree of saturation, mean particle size, test rate, initial void ratio, temperature, confining pressure, and axial strain as input parameters, the Local Interpretable Model-agnostic Explanations (LIME) feature-importance analysis identified initial void ratio as the most influential parameter. The adaptability of the fundamental LSTM shows better accuracy than the Random Forest (RF) model and the Multilayer Perception (MLP) model. To reduce discrepancies between predicted and measured results in the fundamental LSTM model, an uncertainty factor was introduced to improve residual accuracy, thereby facilitating the development of an XGBoost-optimized LSTM (LSTMXGBoost) model. The resulting LSTMXGBoost model demonstrated strong predictive performance for axial strain-deviatoric stress relationship across a range of triaxial shear test conditions. Analysis of results suggest that there is a good comparison between two key parameters; namely, modulus of elasticity and peak stress that were extracted from a separate triaxial test dataset not used in model training or testing. The results of this study are promising for constructing high-dimensional constitutive models that can be used in numerical simulations of frozen sands behavior for use in geotechnical engineering practice applications alleviating time consuming, cumbersome and expensive experimental test techniques.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".