Predicting soil stress–strain behaviour with bidirectional long short-term memory networks
Bibliographic record
Abstract
Purpose Artificial intelligence, particularly deep learning (DL), has increasingly influenced various scientific fields, including soil mechanics. This paper aims to present a novel DL application of long short-term memory (LSTM) networks for predicting soil behaviour during constant rate of strain (CRS) tests. Design/methodology/approach LSTMs are adept at capturing long-term dependencies in sequential data, making them suitable for predicting the complex, nonlinear stress–strain behaviour of soil. This paper evaluates various LSTM configurations, optimising parameters such as step size, batch size, data sampling rate and training subset size to balance prediction accuracy and computational efficiency. The study uses a comprehensive data set from numerical finite element method simulations conducted with PLAXIS 2D and laboratory CRS tests. Findings The proposed LSTM model, trained on data at lower stress levels, accurately forecasts soil behaviour at higher stress levels. The optimal LSTM setup achieved a median error of 3.59% and 5.10% for numerical data and 3.86% for laboratory data, presenting the setup’s effectiveness. Originality/value This approach reduces the required time to complete extensive laboratory testing, aligning with sustainable industrial practices. The findings suggest that LSTM networks can enhance geotechnical engineering applications by efficiently predicting soil behaviour.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".