Structural Health Monitoring Based on Data Science Techniques

<div>Chapter 1. Vibration-based structural damage detection using sparse Bayesian learning techniques (Rongrong Hou).-&nbsp;Chapter 2.&nbsp;Bayesian deep learning for vibration-based bridge damage detection (Davíð Steinar Ásgrímsson).- Chapter 3.&nbsp;Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning (Manuel A. Vega).- Chapter 4.&nbsp;Real-Time Machine Learning for High-Rate Structural Health Monitoring (Simon Laflamme).- Chapter 5.&nbsp;Development and validation of a data-based SHM method for railway bridges (Ana Claudia Neves).- Chapter 6.&nbsp;Real-time unsupervised detection of early damage in railway bridges using traffic-induced responses (Andreia Meixedo).- Chapter 7.&nbsp;Fault diagnosis in structural health monitoring systems using signal processing and machine learning techniques (Henrieke Fritz). Chapter 8.&nbsp;A self-adaptive hybrid model/data-driven approach to SHM based on Model Order Reduction and Deep Learning (Luca Rosafalco).- Chapter 9.&nbsp;Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced order modeling (Caterina Bigoni).- Chapter 10.&nbsp;Unsupervised data-driven methods for damage identification in discontinuous media (Rebecca Napolitano).- Chapter 11.&nbsp;Applications of Deep Learning in intelligent construction (Yang Zhang).- Chapter 12.&nbsp;Integrated SHM systems: Damage detection through unsupervised learning and data fusion (Enrique García-Macías).- Chapter 13.&nbsp;Environmental influence on modal parameters: linear and non-linear methods for its compensation in the context of Structural Health Monitoring (Carlo Rainieri).- Chapter 14.&nbsp;Vibration based damage feature for long-term structural health monitoring under realistic environmental and operational variability (Francescantonio Lucà).- Chapter 15.&nbsp;On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring (David García Cava). Chapter 16.&nbsp;Explainable artificial intelligence to advanced structural health monitoring (Daniel Luckey).- Chapter 17.&nbsp;Physics-informed machine learning for Structural Health Monitoring (Elizabeth J. Cross).- Chapter 18.&nbsp;Interpretable Machine Learning for Function Approximation in Structural Health Monitoring (Jin-Song Pei).- Chapter 19.&nbsp;Partially-Supervised Learning for Data-Driven Structural Health Monitoring (Lawrence A. Bull).- Chapter 20.&nbsp;Population-Based Structural Health Monitoring (Paul Gardner).- Chapter 21.&nbsp;Machine Learning-Based Structural Damage Identification within Three-Dimensional Point Clouds (Mohammad Ebrahim Mohammadi).- Chapter 22.&nbsp;New sensor nodes, cloud and data analytics: case studies on large scale SHM systems (Isabella Alovisi)</div>