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Table of Contents
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 (Davíº 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 data-driven structural health monitoring (David García Cava). Chapter 16. Explainable artificial intelligence to advanced structural health monitoring (Daniel Luckey)
Chapter 17. Physics-informed machine learning for Structural Health Monitoring (Elizabeth J. Cross)
Chapter 18. Interpretable Machine Learning for Function Approximation in Structural Health Monitoring (Jin-Song Pei)
Chapter 19. Partially-Supervised Learning for Data-Driven Structural Health Monitoring (Lawrence A. Bull)
Chapter 20. Population-Based Structural Health Monitoring (Paul Gardner)
Chapter 21. Machine Learning-Based Structural Damage Identification within Three-Dimensional Point Clouds (Mohammad Ebrahim Mohammadi)
Chapter 22. New sensor nodes, cloud and data analytics: case studies on large scale SHM systems (Isabella Alovisi).
Chapter 2. Bayesian deep learning for vibration-based bridge damage detection (Davíº 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 data-driven structural health monitoring (David García Cava). Chapter 16. Explainable artificial intelligence to advanced structural health monitoring (Daniel Luckey)
Chapter 17. Physics-informed machine learning for Structural Health Monitoring (Elizabeth J. Cross)
Chapter 18. Interpretable Machine Learning for Function Approximation in Structural Health Monitoring (Jin-Song Pei)
Chapter 19. Partially-Supervised Learning for Data-Driven Structural Health Monitoring (Lawrence A. Bull)
Chapter 20. Population-Based Structural Health Monitoring (Paul Gardner)
Chapter 21. Machine Learning-Based Structural Damage Identification within Three-Dimensional Point Clouds (Mohammad Ebrahim Mohammadi)
Chapter 22. New sensor nodes, cloud and data analytics: case studies on large scale SHM systems (Isabella Alovisi).