Characterizing the 3D Stress-Strain Behavior of Sandy Soils: A Neuro-Mechanistic Approach
Bibliographic record
Abstract
An adaptive feed-back backpropagation artificial neural network (ANN) approach is used in this study along with proven mechanics-based knowledge/concepts to characterize the complex 3D monotonic stress-strain behavior of a sandy soil. To accomplish this objective, four consecutive modeling stages were conducted. In the first stage, mechanics-based knowledge relating to the cause and effect process (i.e., stress-strain) was used to design an appropriate general purpose stress-controlled ANN model. In the second stage, the designed model with appropriate input categories dictated by mechanics-based knowledge was trained and tested on 3D monotonic stress-strain experimental data of a sandy soil. In the third stage, statistical and graphical accuracy outcomes on training and testing stress-strain responses were used to arrive at the optimal neuro-mechanistic based model. In the last stage, the selected neuro-mechanistic based model is combined with appropriate mechanics-based concepts to create a hybrid neuro-mechanistic simulator (NMS). The developed NMS was found efficient in characterizing the 3D monotonic stress-strain behavior of Canadian River sand subjected to both shear and Hydrostatic Compression (HC) loading stress paths. Consequently, NMS can easily be used to simulate in real time any desired monotonic deformational behavior for given stress path and known initial stress and strain conditions.
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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 | 0.001 |
| 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".