This book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with. For decision-making, innovative distance-based novelty detection techniques are presented to detect, locate, and quantify different damage scenarios. The performance of the presented methods is demonstrated via laboratory and full-scale structures along with several comparative studies. The main target audience of the book includes scholars, graduate students working on SHM via statistical pattern recognition in terms of feature extraction and classification for damage diagnosis under environmental and operational variations; it would also be beneficial for practicing engineers whose work involves these topics.

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Prof. Alireza Entezami has been an assistant professor in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano, Italy, since November 2022. His current role also includes co-supervision of a research project granted by the European Space Agency (ESA), which employs data mining and machine learning techniques for monitoring the structural integrity of large infrastructures using earth observation. Prior to joining the DICA department as a faculty member, he was a post-doctoral research fellowship selected by ESA, working in the DICA at Politecnico di Milano since May 2021. In April 2020, he received a Ph.D. in Structural, Seismic, and Geotechnical Engineering from Politecnico di Milano with Cum Laude degree His research interests span from model-driven structural damage detection to data-driven structural health monitoring, with the focus on large civil infrastructures. Dr. Bahareh Behkamal, a dynamic researcher in the realm of computer science, has been contributing to the fields of artificial intelligence, machine learning, deep learning, and health monitoring of structures through her expertise. Prior to her current engagement, from August 2018 to December 2021, she was a researcher, collaborating with the Department of Applied Science and Technology at Politecnico di Torino, Turin, Italy. Since January 2022, she has been serving as a post-doctoral researcher in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano, contributing to a project focused on the application of artificial intelligence and machine learning in addressing natural hazards and hydrological challenges. Additionally, since April 2023, she has been a post-doctoral research fellowship of the European Space Agency (ESA), continuing her work at DICA, Politecnico di Milano. Prof. Carlo De Michele has been a professor in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano since June2019. He served as an associate professor at Politecnico di Milano from 2008 to 2019, following his tenure as an assistant professor in the same department since 1999. In his current role, he also supervises research sponsored by the European Space Agency (ESA). This project leverages advanced data mining and machine learning methodologies to monitor large-scale infrastructures, utilizing data gathered from earth observation and remote sensing. His research interests are broad and impactful, encompassing statistics, stochastic and multivariate modeling, and climate and environmental variability effects. Prof. De Michele has also made significant contributions to understanding precipitation dynamics, hydrological safety of dams, the water-energy nexus, and compound climate-related extremes. Mentoring has been a crucial part of his career.