Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning: A Practical Strategy via Structural Displacements from ... in Applied Sciences and Technology) - Softcover

Entezami, Alireza; Behkamal, Bahareh; De Michele, Carlo

Softcover

ISBN 10: 303153994X ISBN 13: 9783031539947

Publisher: Springer, 2024

Synopsis

This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs). Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM.

About this title

About the Author

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. 

From the Back Cover

This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs). Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM.

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 303153994X
• ISBN 13: 9783031539947
• Binding: Paperback
• Number of pages: 127