mlsanityData to 7 Oct 2026

CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection

arXiv 2202.07831 ↗First posted 16 Feb 2022cs.LG cs.AIcited by 7

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Abstract (arXiv)

The recent advances in the data science field in the last few decades have benefitted many other fields including Structural Health Monitoring (SHM). Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) methods for vibration-based damage diagnostics of civil structures has been utilized extensively due to the observed high performances in learning from data. Along with diagnostics, damage prognostics is also vitally important for estimating the remaining useful life of civil structures. Currently, AI-based data-driven methods used for damage diagnostics and prognostics centered on historical data of the structures and require a substantial amount of data for prediction models. Although some of these methods are generative-based models, they are used to perform ML or DL tasks such as classification, regression, clustering, etc. after learning the distribution of the data. In this study, a variant of Generative Adversarial Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with Gradient Penalty (CycleWDCGAN-GP) model is developed to investigate the "transition of structural dynamic signature from an undamaged-to-damaged state" and "if this transition can be employed for predictive damage detection". The outcomes of this study demonstrate that the proposed model can accurately generate damaged responses from undamaged responses or vice versa. In other words, it will be possible to understand the damaged condition while the structure is still in a healthy (undamaged) condition or vice versa with the proposed methodology. This will enable a more proactive approach in overseeing the life-cycle performance as well as in predicting the remaining useful life of structures.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 92 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

19 of 92 references.

References · 19

  1. 2016Improved Techniques for Training GANscited by 2,938
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
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  7. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  8. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  9. 2019CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversioncited by 45
  10. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  11. 2017Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networkscited by 30
  12. 2021Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimationcited by 15
7 more
  1. 2019COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networkscited by 13
  2. 2019Spatial Evolutionary Generative Adversarial Networkscited by 10
  3. 2020Generative Adversarial Network for Radar Signal Generationcited by 6
  4. 2021Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detectioncited by 5
  5. 2020A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Imagescited by 4
  6. 2021Generative Adversarial Networks for Labelled Vibration Data Generationcited by 2
  7. 2021Generative Adversarial Networks for Data Generation in Structural Health Monitoringcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 3.

7 papers.

  1. 2024Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniquescited by 2
  2. 2024Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoringcited by 1
  3. 2025Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learningcited by 1
  4. 2025Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Networkcited by 1
  5. 2025A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validationcited by 1
  6. 2022Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoringcited by 0
  7. 2024Deep learning architectures for data-driven damage detection in nonlinear dynamic systemscited by 0