Data Science in Engineering, Volume 9

Ramin Madarshahian, University of San Diego, CA, USA; Francois Hemez, Lawrence Livermore National Laboratory, Livermore, CA, USA.

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