Transfer learning-based artificial neural networks for hysteresis response prediction of steel braces
Bibliographic record
Abstract
• Proposed a novel data-driven model to predict hysteresis response of steel braces. • Used transfer learning with pre-trained baseline LSTM networks for improved performance. • Validated the model using four case studies with experimental and synthetic data. • Applied model in a pseudo-dynamic analysis of a steel braced frame. • Model accurately estimates displacement-force relationship in steel braces. This paper proposes a novel data-driven surrogate model for predicting the hysteresis response, i.e., axial force – axial deformation, of steel braces in concentrically braced frames under seismic loading using transfer learning-based artificial neural networks. Transfer learning is utilized to leverage pre-trained baseline long short-term memory networks and transfer its knowledge to the new hysteresis surrogate model. The proposed model is validated using four case studies involving various combinations of input data obtained from laboratory tests and data generated using random earthquake-induced vibration, featuring a wide range of frequency contents, amplitudes, and durations. A pseudo-dynamic analysis is then performed on a steel braced frame system to demonstrate the application of the proposed surrogate model in system-level response evaluation while verifying the performance of the model in real-time seismic simulations. The results obtained from the validation study confirm that the proposed brace hysteresis model can properly estimate the underlying physical relationship between the input displacement and output force using the transfer learning approach. The proposed model offers an efficient method to evaluate the dynamic response of steel braced frames.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".