| Autore |
Madarshahian Ramin
|
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
|
| Descrizione fisica |
1 online resource (287 pages)
|
| Disciplina |
620.00151
|
| Collana |
Conference Proceedings of the Society for Experimental Mechanics Series
|
| Soggetto topico |
Engineering mathematics
Engineering - Data processing
Artificial intelligence - Data processing
Civil engineering
Mechanical engineering
Mathematical and Computational Engineering Applications
Data Science
Civil Engineering
Mechanical Engineering
|
| ISBN |
3-030-76004-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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.
|
| Record Nr. | UNINA-9910522921503321 |
Info
Data Science in Engineering, Volume 10 : Proceedings of the 41st IMAC, a Conference and Exposition on Structural Dynamics 2023
