Data Science in Engineering, Volume 9 Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021

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

Biography

Ramin Madarshahian, University of California, San Diego, San Diego, USA. Francois Hemez, Department of Energy-Defense Programs, Lawrence Livermore National Laboratory, Livermore, USA.