Structural Health Monitoring Based on Data Science Techniques

Chapter 1. Vibration-based structural damage detection using sparse Bayesian learning techniques (Rongrong Hou).- Chapter 2. Bayesian deep learning for vibration-based bridge damage detection (Davíð Steinar Ásgrímsson).- Chapter 3. Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning (Manuel A. Vega).- Chapter 4. Real-Time Machine Learning for High-Rate Structural Health Monitoring (Simon Laflamme).- Chapter 5. Development and validation of a data-based SHM method for railway bridges (Ana Claudia Neves).- Chapter 6. Real-time unsupervised detection of early damage in railway bridges using traffic-induced responses (Andreia Meixedo).- Chapter 7. Fault diagnosis in structural health monitoring systems using signal processing and machine learning techniques (Henrieke Fritz). Chapter 8. A self-adaptive hybrid model/data-driven approach to SHM based on Model Order Reduction and Deep Learning (Luca Rosafalco).- Chapter 9. Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced order modeling (Caterina Bigoni).- Chapter 10. Unsupervised data-driven methods for damage identification in discontinuous media (Rebecca Napolitano).- Chapter 11. Applications of Deep Learning in intelligent construction (Yang Zhang).- Chapter 12. Integrated SHM systems: Damage detection through unsupervised learning and data fusion (Enrique García-Macías).- Chapter 13. Environmental influence on modal parameters: linear and non-linear methods for its compensation in the context of Structural Health Monitoring (Carlo Rainieri).- Chapter 14. Vibration based damage feature for long-term structural health monitoring under realistic environmental and operational variability (Francescantonio Lucà).- Chapter 15. On explicit and implicit procedures to mitigate environmental and operational variabilities in