Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning / SpringerBriefs in Applied Sciences and Technology (PDF)
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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.
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
- Autoren: Alireza Entezami , Bahareh Behkamal , Carlo De Michele
- 2024, 2024, 110 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031539958
- ISBN-13: 9783031539954
- Erscheinungsdatum: 21.02.2024
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- Dateiformat: PDF
- Größe: 14 MB
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