Comparison of deep learning techniques for prediction of stress distribution in stiffened panels
Bibliographic record
Abstract
Compared to the finite element method (FEM), surrogate models for structural analysis enable more efficient assessment of a response under loads and subsequent optimization. Recent advancements in deep learning have allowed use of neural networks as surrogate models in a variety of fields with astonishing results. Nonetheless, their use for predicting stress distribution in stiffened panels is unexplored. Predicting stress fields is important for various limit states. We propose an approach to encode stiffened panels with various geometries into grid spaces, which can then be processed by a convolutional neural network (CNN). Uniform pressure and patch loading are considered. The performance of CNN using the proposed modeling approach is compared to multilayer perceptron (MLP) in predicting von Mises stress distribution in stiffened panels. Principle component analysis (PCA) is used to reduce the training complexity for MLP. Moreover, the effect of skip connections is investigated utilizing two different CNN architectures. Five case studies are conducted to assess the performance of these neural networks in predicting the stress distribution in stiffened panels across various geometric configurations, including variations in the number of stiffeners, loading and boundary conditions. The study reveals that CNNs, particularly with skip connections (U-Net), outperform MLP, achieving less than 5% mean absolute percentage error with respect to FEM results in all cases. MLP with PCA achieves satisfactory results for simpler problems, but cannot be trained for more complex tasks. CNNs effectively capture local stress variations in all cases. CNNs have good capability in predicting stress distribution with limited amount of data, making them a viable tool for real-world structural analysis.
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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) | 0.000 | 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 | 0.001 |
| 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".