LEADER 03311nam  22006735  450 
001 9910869156003321
005 20240630125243.0
010   $a9783031561283
024 7 $a10.1007/978-3-031-56128-3
035   $a(CKB)32609879000041
035   $a(MiAaPQ)EBC31507278
035   $a(Au-PeEL)EBL31507278
035   $a(DE-He213)978-3-031-56128-3
035   $a(OCoLC)1443934629
035   $a(EXLCZ)9932609879000041
100   $a20240630d2024      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBayesian Network Modeling of Corrosion /$fedited by Narasi Sridhar
205   $a1st ed. 2024.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024.
215   $a1 online resource (343 pages)
311 08$a9783031561276 
327   $aChapter1. 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.
330   $aThis 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.
606   $aCorrosion and anti-corrosives
606   $aStatistics
606   $aStochastic models
606   $aSurfaces (Technology)
606   $aThin films
606   $aCoatings
606   $aCorrosion
606   $aBayesian Network
606   $aStochastic Modelling in Statistics
606   $aSurfaces, Interfaces and Thin Film
606   $aCoatings
615  0$aCorrosion and anti-corrosives.
615  0$aStatistics.
615  0$aStochastic models.
615  0$aSurfaces (Technology)
615  0$aThin films.
615  0$aCoatings.
615 14$aCorrosion.
615 24$aBayesian Network.
615 24$aStochastic Modelling in Statistics.
615 24$aSurfaces, Interfaces and Thin Film.
615 24$aCoatings.
676   $a620.11223
700   $aSridhar$b Narasi$01743639
701   $aSridhar$01743640
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910869156003321
996   $aBayesian Network Modeling of Corrosion$94171894
997   $aUNINA