04169nam a22005775i 4500 978-3-031-53995-4 DE-He213 20251030111436.0 cr nn 008mamaa 240221s2024 sz | s |||| 0|eng d 9783031539954 978-3-031-53995-4 10.1007/978-3-031-53995-4 doi TA401-492 TGM bicssc TEC021000 bisacsh TGM thema Entezami, Alireza. author. aut http://id.loc.gov/vocabulary/relators/aut 71478 Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning [electronic resource] : A Practical Strategy via Structural Displacements from Synthetic Aperture Radar Images / by Alireza Entezami, Bahareh Behkamal, Carlo De Michele. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVII, 110 p. 42 illus., 40 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda PoliMI SpringerBriefs, 2282-2585 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. 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. Building materials. Machine learning. 55059 Buildings Design and construction. Engineering Data processing. Structural Materials. 65620 Machine Learning. 55059 Building Construction and Design. Data Engineering. 66059 Behkamal, Bahareh. author. aut http://id.loc.gov/vocabulary/relators/aut 71479 De Michele, Carlo. author. aut http://id.loc.gov/vocabulary/relators/aut 71480 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031539947 Printed edition: 9783031539961 PoliMI SpringerBriefs, 2282-2585 66262 https://doi.org/10.1007/978-3-031-53995-4 ZDB-2-CMS ZDB-2-SXC 57780 57780