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

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.

Table of Contents:
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.

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