Deep Learning‐Based Response Spectrum Analysis Method for Bridges Subjected to Bi‐Directional Ground Motions
Bibliographic record
Abstract
ABSTRACT The response spectrum analysis method is one of the most widely used approaches developed to estimate the seismic demands of structural systems with minimal computational expense while maintaining high accuracy. The authors recently proposed a deep learning‐based combination (DC) rule to enhance the prediction accuracy of the response spectrum analysis method without compromising computational efficiency. The DC rule employs a deep neural network (DNN) model to estimate the contributions of individual modal responses. The DC rule, primarily developed for building structural systems, has limitations in its applications to bridge structures, particularly those subjected to bi‐directional ground motions. Moreover, the inherent “black box” nature of deep learning models restricts the interpretability and practicality of the method. To address these challenges, this research further develops the DC rule in three aspects. First, we construct a seismic demand database for bridge structures subjected to bi‐directional ground motions. Second, the DC rule is extended to accommodate structural systems under bi‐directional ground motion excitations. Third, we develop a simplified regression‐based model to replace the DNN model, thereby enhancing the practicality and interpretability of the DC rule approach. Extensive numerical investigations are conducted to validate the performance of the proposed framework, demonstrating its efficiency and accuracy in predicting the seismic demands of bridge structures. The source codes, data, and trained DNN models are available for download at https://github.com/TyongKim/ERD2 .
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".