A Practical Strategy via Structural Displacements from Synthetic Aperture Radar Images Entezami, Alireza. creator author. aut http://id.loc.gov/vocabulary/relators/aut Behkamal, Bahareh. author. aut http://id.loc.gov/vocabulary/relators/aut De Michele, Carlo. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access XVII, 110 p. 42 illus., 40 illus. in color. online resource. 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. Pioneering Remote Sensing in Structural Health Monitoring -- Advanced ML Methods: Bridging SAR Images and Structural Health Monitoring -- Simulating Reality: Numerical Assessments of a Bridge Health Monitoring -- From Theory to Reality: Advanced SHM Methods to the Tadcaster Bridge -- Conclusions and Prospects for Structural Health Monitoring. by Alireza Entezami, Bahareh Behkamal, Carlo De Michele. Building materials Machine learning Buildings Design and construction Engineering Data processing Structural Materials Machine Learning Building Construction and Design Data Engineering TA401-492 9783031539954 https://doi.org/10.1007/978-3-031-53995-4 https://doi.org/10.1007/978-3-031-53995-4 240221 20251030111436.0 978-3-031-53995-4