Prediction of hysteresis response of steel braces using long Short-Term memory artificial neural networks
Why this work is in the frame
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Bibliographic record
Abstract
• Proposed ANN-based surrogate models for the nonlinear hysteresis response prediction of steel buckling-restrained and conventional hollow structural section braces. • Utilized long short-term memory (LSTM) algorithm for signal-to-signal prediction in proposed surrogate models. • Developed a decoupling technique to overcome the limited experimental datasets. • Validated the steel brace surrogate models using experimental and synthetic numerical data. This article proposes artificial neural networks that utilize the long short-term memory (LSTM) algorithm to estimate the nonlinear hysteresis response of steel buckling-restrained and conventional hollow structural section braces. The proposed models overcome the two main challenges: 1) the complexity of hysteresis response (tensile yielding and strain-hardening in tension, and compressive buckling and strength degradation in compression) and 2) limited training data, using an LSTM network and auxiliary parameters. The development of a suitable training dataset is first presented. The architectures of the proposed models are then described followed by the validation of the model against unseen brace hysteresis responses. The validation results confirm that the proposed LSTM networks are both accurate and computationally efficient in predicting the response of steel braces to random lateral loads, namely axial force – axial deformation response. The proposed models have the potential to be used for seismic response evaluation of steel braced frames, provided that their limitations are properly considered.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it