Seismic Response Estimation and Performance Assessment of Large-Scale Structures Using Deep-Learning Methods
Bibliographic record
Abstract
Applications of machine learning (ML) in structural engineering and mechanics have increased substantially in recent years. Interest in ML stems from its potential to handle complex problems with increased accuracy and/or computational efficiency compared with traditional methods when a sufficient amount of data is available. This study explored the application of deep neural networks to seismic response and damage estimation for large-scale civil structures in the context of performance-based earthquake engineering. The objective was to experimentally validate the accuracy and efficiency of modified versions of the long short-term memory (LSTM) neural network and the physics-guided convolutional neural network (PhyCNN) in structures subjected to base excitations in linear and nonlinear response regimes. In particular, the robustness of the methods to predict the response and state of integrity of structures undergoing severe damage due to strong base motions when the methods are trained using limited low-amplitude linear input–output data was assessed. The performance and predicting capabilities of the methods were evaluated using experimental data from two full-scale case studies: a six-story building structure in San Bernardino, California, instrumented as part of the California Strong Motion Instrumentation Program (CSMIP), and a seven-story full-scale shear wall structure tested on the high-performance outdoor shake table at the University of California at San Diego. The results show that deep neural networks have limited robustness when applied to estimate response features outside the training domain, with a significant reduction in the prediction accuracy if the level of nonlinearity for prediction is higher than in the training data set.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".