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001442535 001__ 1442535
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001442535 019__ $$a1280458642$$a1280603543$$a1281136590$$a1287774120$$a1296666913
001442535 020__ $$a9783030817169$$q(electronic bk.)
001442535 020__ $$a3030817164$$q(electronic bk.)
001442535 020__ $$z9783030817152$$q(print)
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001442535 0247_ $$a10.1007/978-3-030-81716-9$$2doi
001442535 035__ $$aSP(OCoLC)1281147469
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001442535 049__ $$aISEA
001442535 050_4 $$aTA656.6
001442535 08204 $$a624.1/7$$223
001442535 24500 $$aStructural health monitoring based on data science techniques /$$cAlexandre Cury, Diogo Ribeiro, Filippo Ubertini, Michael D. Todd, editors.
001442535 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001442535 300__ $$a1 online resource (xv, 484 pages) :$$billustrations (some color)
001442535 336__ $$atext$$btxt$$2rdacontent
001442535 337__ $$acomputer$$bc$$2rdamedia
001442535 338__ $$aonline resource$$bcr$$2rdacarrier
001442535 347__ $$atext file
001442535 347__ $$bPDF
001442535 4901_ $$aStructural integrity,$$x2522-5618 ;$$vvolume 21
001442535 5050_ $$aChapter 1. Vibration-based structural damage detection using sparse Bayesian learning techniques (Rongrong Hou) -- Chapter 2. Bayesian deep learning for vibration-based bridge damage detection (Davíº Steinar Ásgrímsson) -- Chapter 3. Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning (Manuel A. Vega) -- Chapter 4. Real-Time Machine Learning for High-Rate Structural Health Monitoring (Simon Laflamme) -- Chapter 5. Development and validation of a data-based SHM method for railway bridges (Ana Claudia Neves) -- Chapter 6. Real-time unsupervised detection of early damage in railway bridges using traffic-induced responses (Andreia Meixedo) -- Chapter 7. Fault diagnosis in structural health monitoring systems using signal processing and machine learning techniques (Henrieke Fritz). Chapter 8. A self-adaptive hybrid model/data-driven approach to SHM based on Model Order Reduction and Deep Learning (Luca Rosafalco) -- Chapter 9. Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced order modeling (Caterina Bigoni) -- Chapter 10. Unsupervised data-driven methods for damage identification in discontinuous media (Rebecca Napolitano) -- Chapter 11. Applications of Deep Learning in intelligent construction (Yang Zhang) -- Chapter 12. Integrated SHM systems: Damage detection through unsupervised learning and data fusion (Enrique García-Macías) -- Chapter 13. Environmental influence on modal parameters: linear and non-linear methods for its compensation in the context of Structural Health Monitoring (Carlo Rainieri) -- Chapter 14. Vibration based damage feature for long-term structural health monitoring under realistic environmental and operational variability (Francescantonio Lucà) -- Chapter 15. On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring (David García Cava). Chapter 16. Explainable artificial intelligence to advanced structural health monitoring (Daniel Luckey) -- Chapter 17. Physics-informed machine learning for Structural Health Monitoring (Elizabeth J. Cross) -- Chapter 18. Interpretable Machine Learning for Function Approximation in Structural Health Monitoring (Jin-Song Pei) -- Chapter 19. Partially-Supervised Learning for Data-Driven Structural Health Monitoring (Lawrence A. Bull) -- Chapter 20. Population-Based Structural Health Monitoring (Paul Gardner) -- Chapter 21. Machine Learning-Based Structural Damage Identification within Three-Dimensional Point Clouds (Mohammad Ebrahim Mohammadi) -- Chapter 22. New sensor nodes, cloud and data analytics: case studies on large scale SHM systems (Isabella Alovisi).
001442535 506__ $$aAccess limited to authorized users.
001442535 520__ $$aThe modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of "big data" availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world
001442535 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 29, 2021).
001442535 650_0 $$aStructural health monitoring.
001442535 650_0 $$aStructural health monitoring$$xData processing.
001442535 650_6 $$aSurveillance de l'état des structures.
001442535 650_6 $$aSurveillance de l'état des structures$$xInformatique.
001442535 655_0 $$aElectronic books.
001442535 7001_ $$aCury, Alexandre,$$eeditor$$0(orcid)0000-0002-8860-1286$$1https://orcid.org/0000-0002-8860-1286
001442535 7001_ $$aRibeiro, Diogo,$$eeditor$$0(orcid)0000-0001-8624-9904$$1https://orcid.org/0000-0001-8624-9904
001442535 7001_ $$aUbertini, Filippo,$$eeditor$$0(orcid)0000-0002-5044-8482$$1https://orcid.org/0000-0002-5044-8482
001442535 7001_ $$aTodd, Michael D.,$$eeditor$$0(orcid)0000-0002-4492-5887$$1https://orcid.org/0000-0002-4492-5887
001442535 77608 $$iPrint version:$$tStructural health monitoring based on data science techniques.$$dCham, Switzerland : Springer, [2022]$$z3030817156$$z9783030817152$$w(OCoLC)1257890455
001442535 830_0 $$aStructural integrity (Series) ;$$vv. 21.$$x2522-5618
001442535 852__ $$bebk
001442535 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-81716-9$$zOnline Access$$91397441.1
001442535 909CO $$ooai:library.usi.edu:1442535$$pGLOBAL_SET
001442535 980__ $$aBIB
001442535 980__ $$aEBOOK
001442535 982__ $$aEbook
001442535 983__ $$aOnline
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