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Data Science in Engineering, Volume 9 Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021
Title
Data Science in Engineering, Volume 9 [electronic resource] : Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021 / edited by Ramin Madarshahian, Francois Hemez.
ISBN
9783030760045
Edition
1st ed. 2022.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2022.
Physical Description
1 online resource (VIII, 291 p.) 225 illus., 198 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
January 27, 2022
Series
Conference Proceedings of the Society for Experimental Mechanics Series,
Contents
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 to Persistent 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.
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