03177nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001700172072001600189072002300205072001500228245009500243250001800338264007500356300006700431336002600498337002600524338003600550347002400586505049100610520088201101650004201983650002502025650002302050650003402073650002302107650001402130650002202144650002902166650004702195650004702242650001402289700008502303710003402388773002602422776003602448776003602484776003602520856004602556912001402602912001402616999001702630978-3-031-56128-3DE-He21320251030112342.0cr nn 008mamaa240630s2024    sz |    s    |||| 0|eng d  a97830315612839978-3-031-56128-37 a10.1007/978-3-031-56128-32doi 4aTA418.74-.76 7aTGM2bicssc 7aTEC0210402bisacsh 7aTGM2thema10aBayesian Network Modeling of Corrosionh[electronic resource] /cedited by Narasi Sridhar.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXIV, 338 p. 147 illus., 140 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. 0aCorrosion and anti-corrosives.966554 0aStatistics .965982 0aStochastic models. 0aSurfaces (Technology).965847 0aThin films.965848 0aCoatings.14aCorrosion.96655724aBayesian Network.97966724aStochastic Modelling in Statistics.96672024aSurfaces, Interfaces and Thin Film.96585024aCoatings.1 aSridhar, Narasi.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9796682 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303156127608iPrinted edition:z978303156129008iPrinted edition:z978303156130640uhttps://doi.org/10.1007/978-3-031-56128-3  aZDB-2-CMS  aZDB-2-SXC  c61176d61176