Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning A Practical Strategy via Structural Displacements from Synthetic Aperture Radar Images /
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...
Saved in:
| Main Authors: | , , |
|---|---|
| 企业作者: | |
| 格式: | 电子 电子书 |
| 语言: | 英语 |
| 出版: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
|
| 版: | 1st ed. 2024. |
| 丛编: | PoliMI SpringerBriefs,
|
| 主题: | |
| 在线阅读: | https://doi.org/10.1007/978-3-031-53995-4 |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-53995-4 | ||
| 003 | DE-He213 | ||
| 005 | 20260810155438.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240221s2024 sz | o |||| 0|eng d | ||
| 020 | |a 9783031539954 |9 978-3-031-53995-4 | ||
| 024 | 7 | |a 10.1007/978-3-031-53995-4 |2 doi | |
| 050 | 4 | |a TA401-492 | |
| 072 | 7 | |a TGM |2 bicssc | |
| 072 | 7 | |a TEC021000 |2 bisacsh | |
| 072 | 7 | |a TGM |2 thema | |
| 082 | 0 | 4 | |a 620.1 |2 23 |
| 100 | 1 | |a Entezami, Alireza. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning |h [electronic resource] : |b A Practical Strategy via Structural Displacements from Synthetic Aperture Radar Images / |c by Alireza Entezami, Bahareh Behkamal, Carlo De Michele. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XVII, 110 p. 42 illus.). |b 40 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a PoliMI SpringerBriefs, |x 2282-2585 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Building materials. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Buildings |x Design and construction. | |
| 650 | 0 | |a Engineering |x Data processing. | |
| 650 | 1 | 4 | |a Structural Materials. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Building Construction and Design. |
| 650 | 2 | 4 | |a Data Engineering. |
| 700 | 1 | |a Behkamal, Bahareh. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a De Michele, Carlo. |e author. |0 (orcid)0000-0002-7098-4725 |1 https://orcid.org/0000-0002-7098-4725 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031539947 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031539961 |
| 830 | 0 | |a PoliMI SpringerBriefs, |x 2282-2585 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-53995-4 |
| 912 | |a ZDB-2-CMS | ||
| 912 | |a ZDB-2-SXC | ||
| 950 | |a Chemistry and Materials Science (SpringerNature-11644) | ||
| 950 | |a Chemistry and Material Science (R0) (SpringerNature-43709) | ||