01048nam0 22002771i 450 UON0045143920231205105043.17720150309d1960 |0itac50 barusRU|||| 1||||Izbrannye stat'iI. LežnevMoskvaChudožestvennaja literatura1960324 p.21 cm.ŠOLOCHOV MICHAIL ALEKSANDROVIČUONC038574FIČERNYŠEVSKIJ NIKOLAJ GAVRILOVIČUONC040394FIKRYMOV JURIJUONC087016FI891.703Letteratura russa. 1800-191721891.704Letteratura russa, 1917-21LEŽNEVIsaj Grigor'evičUONV225358ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00451439SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI RUSSO C 0648 SI SLA170 7 0648 Izbrannye stat'i1330130UNIOR03370nam 22006855 450 991086915600332120250807135523.0978303156128310.1007/978-3-031-56128-3(CKB)32609879000041(MiAaPQ)EBC31507278(Au-PeEL)EBL31507278(DE-He213)978-3-031-56128-3(OCoLC)1443934629(EXLCZ)993260987900004120240630d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierBayesian Network Modeling of Corrosion /edited by Narasi Sridhar1st ed. 2024.Cham :Springer International Publishing :Imprint: Springer,2024.1 online resource (343 pages)Chemistry and Materials Science Series9783031561276 Chapter1. Introduction: Risk Assessment -- Chapter.2. Bayesian Network Basics -- Chaoter.3. Corrosion Models -- Chapter.4. Statistical Models: Propagation of Uncertainty and Monte Carlo modeling -- Chapter.5. Corrosion Risk Assessment in Pipelines -- Chapter.6. Oil and Gas Production Systems -- Chapter.7.Nuclear Energy -- Chapter.8. Localized Corrosion in Saline Environments -- Chapter.9. BN for reinforced concrete structures -- Chapter.10.Coatings -- Chapter.11.Summary and Future.This book represents a compilation of experience from a slate of experts involved in developing and deploying Bayesian Networks (BN) for corrosion management. The contributors describe how probability distributions can be developed for corroding systems and BN can be applied as an ideal framework to deal with corrosion risk. Corrosion can develop suddenly and grow rapidly after a long incubation period and take many non-uniform aspects, including pitting and stress corrosion cracking, that cannot be mitigated by simply bulking up the system. They also describe how complex engineering structures and systems are influenced by many natural and engineering factors that come together in myriad ways. It provides a broad perspective to the reader on the potential of BN as an artificial intelligence tool for corrosion risk management and the challenges for implementing it.Corrosion and anti-corrosivesStatisticsStochastic modelsSurfaces (Technology)Thin filmsCoatingsCorrosionBayesian NetworkStochastic Modelling in StatisticsSurfaces, Interfaces and Thin FilmCoatingsCorrosion and anti-corrosives.Statistics.Stochastic models.Surfaces (Technology)Thin films.Coatings.Corrosion.Bayesian Network.Stochastic Modelling in Statistics.Surfaces, Interfaces and Thin Film.Coatings.620.11223Sridhar Narasi1743639Śrīdhar1743640MiAaPQMiAaPQMiAaPQ9910869156003321Bayesian Network Modeling of Corrosion4171894UNINA