Neural Network for Constitutive Modeling of Beam Structures
Bibliographic record
Abstract
Abstract Constitutive modeling plays a vital role in simulating the stress-strain behavior of structures within various engineering applications. Traditionally, constitutive models relied on experimental tests to accurately characterize the stress-strain relationship. In recent decades, there has been a significant increase in the development of advanced constitutive laws due to the high cost associated with testing. However, numerical computations based on advanced constitutive laws heavily relies on numerical integration which is time-consuming and computationally demanding. This leads to the need to develop an alternate approach. In this study, a neural network-based constitutive model using TensorFlow was proposed to capture the non-linear relationship between stress and strain in beam structures. A case study was conducted where the neural network-based constitutive model was implemented to predict the axial force and bending moment of the pipeline subjected to ground displacement, modeled as an Euler-Bernoulli beam with large deformations. The comparative analysis with traditional numerical integration schemes was pursued to assess the performance of the proposed model. The results showed a significant reduction in computation time when dealing with large data sizes. Additionally, the impacts of the training data samples on the applicability of the neural network constitutive model were investigated. The findings of this study support that the neural network-based constitutive model serve as a more efficient tool for extensive numerical simulations in the design and structural analysis of beam structures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".