000			04169nam a22005775i 4500
001			978-3-031-53995-4
003			DE-He213
005			20251030111436.0
007			cr nn 008mamaa
008			240221s2024 sz | s |||| 0|eng d
020			_a9783031539954 _9978-3-031-53995-4
024	7		_a10.1007/978-3-031-53995-4 _2doi
050		4	_aTA401-492
072		7	_aTGM _2bicssc
072		7	_aTEC021000 _2bisacsh
072		7	_aTGM _2thema
100	1		_aEntezami, Alireza. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _971478
245	1	0	_aLong-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning _h[electronic resource] : _bA Practical Strategy via Structural Displacements from Synthetic Aperture Radar Images / _cby Alireza Entezami, Bahareh Behkamal, Carlo De Michele.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024.
300			_aXVII, 110 p. 42 illus., 40 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aPoliMI SpringerBriefs, _x2282-2585
505	0		_aPioneering 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			_aThis 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.
650		0	_aBuilding materials.
650		0	_aMachine learning. _955059
650		0	_aBuildings _xDesign and construction.
650		0	_aEngineering _xData processing.
650	1	4	_aStructural Materials. _965620
650	2	4	_aMachine Learning. _955059
650	2	4	_aBuilding Construction and Design.
650	2	4	_aData Engineering. _966059
700	1		_aBehkamal, Bahareh. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _971479
700	1		_aDe Michele, Carlo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _971480
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031539947
776	0	8	_iPrinted edition: _z9783031539961
830		0	_aPoliMI SpringerBriefs, _x2282-2585 _966262
856	4	0	_uhttps://doi.org/10.1007/978-3-031-53995-4
912			_aZDB-2-CMS
912			_aZDB-2-SXC
999			_c57780 _d57780