Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:
Data Science in Engineering Applications
Engineering Mathematics
Computational Methods in Engineering
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1. Towards a Population-based Structural Health Monitoring, Part V: Networks and Databases.
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2. Active Learning of Post-Earthquake Structural Damage with Co-Optimal Information Gain and Reconnaissance Cost.
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3. Uncertainty-Quantified Damage Identification for High-Rate Dynamic Systems.
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4. Real-time Machine Learning of Vibration Signals.
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5. Data-Driven Identification of Mistuning in Blisks.
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6. On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks.
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7. Best Paper: On an Application of Graph Neural Networks in Population Based SHM.
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8. Estimation of Elastic Band Gaps Using Data-Driven Model.
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9. Damage Localization on Lightweight Structures with Non-Destructive Testing and Machine Learning Techniques.
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10. Challenges for SHM from Structural Repairs: An Outlier-informed Domain Adaptation Approach.
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11. On the Application of Heterogeneous Transfer Learning to Population-based Structural Health Monitoring.
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12. An Unsupervised Deep Auto-Encoder with One-Class Support Vector Machine for Damage Detection.
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13. Identifying Operations- and Environmental-Insensitive Damage Features.
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14. Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds without Big Training Dataset.
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15. Digital Stroboscopy using Event-Driven Imagery.
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16. Managing System Inspections for Health Monitoring: A Probability of Query Approach.
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17. Parameter Estimation for Dynamical Systems Under Continuous and Discontinuous Gaussian Noise Using Data Assimilation Techniques.
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18. Model Reduction of Geometrically Nonlinear Structures via Physics-Informed Autoencoders.
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19. Techniques to Improve Robustness of Video-Based Sensor Networks.
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20. Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems.
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21. On Topological Data Analysis for SHM; An Introduction to Persistent Homology.
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22. Heteroscedastic Gaussian Processes for Localising Acoustic Emission.
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23. Transferring Damage Detectors Between Tailplane Experiments.
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24. High-Rate Structural Health Monitoring and Prognostics: An Overview.
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25. One Versus All: Best Practices in Combining Multi-Hazard Damage Imagery Training Datasets for Damage Detection for a Deep Learning Neural Network.
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26. High-Rate Damage Classification and Lifecycle Prediction via Deep Learning.
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27. A Generalized Technique for Full-field Blind Identification of Travelling Waves and Complex Modes from Video Measurements with Hilbert Transform.
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28. Privacy-Preserving Structural Dynamics.
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29. Abnormal Behavior Detection of the Indian River Inlet Bridge through Cross Correlation Analysis of Truck Induced Strains.
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30. A Video-Based Crack Detection in Concrete Surfaces.
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31. Bayesian Graph Neural Networks for Strain-Based Crack Localization.
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32. Routing of Public and Electric Transportation Systems Using Reinforcement Learning.
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33. Vibration based Damage Detection and Identification in a CFRP Truss with Deep Learning and Finite Element Generated Data.
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34. Parametric Amplification in a Stochastic Nonlinear Piezoelectric Energy Harvester via Machine Learning.
Ramin Madarshahian, University of San Diego, CA, USA; Francois Hemez, Lawrence Livermore National Laboratory, Livermore, CA, USA.
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