mlsanityData to 7 Oct 2026

Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring

arXiv 2212.14048 ↗First posted 28 Dec 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.

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References · 27

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  4. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  5. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  6. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  7. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  8. 2021Domain Generalization: A Surveycited by 760
  9. 2016Language Modeling with Gated Convolutional Networkscited by 688
  10. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  11. 2017Wasserstein GANcited by 635
  12. 2013Domain Generalization via Invariant Feature Representationcited by 622
15 more
  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  2. 2018Learning Models with Uniform Performance via Distributionally Robust Optimizationcited by 334
  3. 2018Domain Generalization via Conditional Invariant Representationcited by 146
  4. 2019Generalizing to unseen domains via distribution matchingcited by 109
  5. 2021Heterogeneous Risk Minimizationcited by 90
  6. 2021Domain Invariant Representation Learning with Domain Density Transformationscited by 53
  7. 2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34
  8. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  9. 2019A Generalization Error Bound for Multi-class Domain Generalizationcited by 26
  10. 2021Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spacescited by 15
  11. 2022On statistic alignment for domain adaptation in structural health monitoringcited by 8
  12. 2022CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detectioncited by 7
  13. 2021Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detectioncited by 5
  14. 2021Generative Adversarial Networks for Labelled Vibration Data Generationcited by 2
  15. 2021Generative Adversarial Networks for Data Generation in Structural Health Monitoringcited by 2

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