| 书目名称 | Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning | | 副标题 | A Practical Strategy | | 编辑 | Alireza Entezami,Bahareh Behkamal,Carlo De Michele | | 视频video | | | 丛书名称 | SpringerBriefs in Applied Sciences and Technology | | 图书封面 |  | | 描述 | .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 r | | 出版日期 | Book 2024 | | 关键词 | Structural Health Monitoring; SHM; environmental and operational changes; civil structures; Hamiltonian | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-53995-4 | | isbn_softcover | 978-3-031-53994-7 | | isbn_ebook | 978-3-031-53995-4Series ISSN 2191-530X Series E-ISSN 2191-5318 | | issn_series | 2191-530X | | copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 |
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发表于 2025-3-21 18:05:20
