Data‐Driven Detection of Internal Erosion Initiation in Gap‐Graded Soils: Combining Particle‐Scale CFD‐DEM Simulation With 3D Convolutional Autoencoder
Bibliographic record
Abstract
ABSTRACT Internal erosion in gap‐graded soils poses significant risks to water‐retaining structures such as earth dams. However, its underlying mechanisms at the particle scale remain poorly understood. This study couples the discrete element method (DEM) with computational fluid dynamics (CFD) to simulate internal erosion in gap‐graded soil assemblies and employs data‐driven techniques to detect early‐stage erosion. Particle‐scale parameters, such as contact forces, particle velocity and fluid velocity, are extracted from the transient CFD‐DEM simulations. These features are transformed into multi‐dimensional voxel‐based tensors representing the particle–fluid interactions, which are used to train deep learning models. Autoencoder models with 3D convolutional neural network (CNN) layers as encoder and decoder are developed to investigate the micro‐scale patterns within the particle‐fluid assembly. Through sequential training techniques, the temporal evolution of anomalies is captured, enabling identification of the initiation point of internal erosion. The results reveal how microscale behaviours, such as particle motion, contact forces and contact number, contribute to macroscale erosion processes. The outcome of this research can inspire further research into AI‐based early detection techniques for internal erosion in earth dams.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".