Sridhar, Narasi. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XIV, 338 p. 147 illus., 140 illus. in color. online resource. 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. 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. edited by Narasi Sridhar. Corrosion and anti-corrosives Statistics  Stochastic models Surfaces (Technology) Thin films Coatings Corrosion Bayesian Network Stochastic Modelling in Statistics Surfaces, Interfaces and Thin Film Coatings TA418.74-.76 9783031561283 https://doi.org/10.1007/978-3-031-56128-3 https://doi.org/10.1007/978-3-031-56128-3 240630 20251030112342.0 978-3-031-56128-3