Structural dynamic response synthesis: A transformer-based time-series GAN approach
This study introduces a novel Transformer-based Time-Series Generative Adversarial Network (TTS-GAN) for synthesizing structural dynamic responses, addressing the critical challenge of data scarcity in structural health monitoring (SHM). Unlike existing methods, TTS-GAN generates realistic multi-cha...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025016196 |
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| Summary: | This study introduces a novel Transformer-based Time-Series Generative Adversarial Network (TTS-GAN) for synthesizing structural dynamic responses, addressing the critical challenge of data scarcity in structural health monitoring (SHM). Unlike existing methods, TTS-GAN generates realistic multi-channel acceleration signals directly from random noise vectors, without requiring auxiliary time- or frequency-domain inputs. The model integrates a transformer architecture with an attention mechanism to capture complex temporal dependencies and employs a composite loss function that aligns generated outputs with real signals in both time and frequency domains. Validation on a three-story aluminum structure demonstrates that TTS-GAN accurately replicates key structural features, including amplitude distribution and natural frequencies. Comparative results confirm that TTS-GAN outperforms a baseline Time-Series GAN (TSGAN), particularly in frequency-domain fidelity. The proposed approach represents a novel and efficient data augmentation framework for SHM, enabling high-quality signal generation from limited measured data. |
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| ISSN: | 2590-1230 |