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Bayesian network modeling of corrosion

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian network modeling of corrosion/ edited by Narasi Sridhar.
other author:	Sridhar, Narasi.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xiv, 338 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1007/978-3-031-56128-3
ISBN:	9783031561283

Bayesian network modeling of corrosion
Bayesian network modeling of corrosion[electronic resource] /edited by Narasi Sridhar. - Cham :Springer International Publishing :2024. - xiv, 338 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783031561283

Standard No.: 10.1007/978-3-031-56128-3doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: TA418.74

Dewey Class. No.: 620.11223

Bayesian network modeling of corrosion
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245 0 0 $a Bayesian network modeling of corrosion $h [electronic resource] / $c edited by Narasi Sridhar.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xiv, 338 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Corrosion and anti-corrosives $x Statistical methods. $3 3718584
650 1 4 $a Corrosion. $3 3595443
650 2 4 $a Bayesian Network. $3 3603368
650 2 4 $a Stochastic Modelling in Statistics. $3 3599029
650 2 4 $a Surfaces, Interfaces and Thin Film. $3 3593580
650 2 4 $a Coatings. $3 558166
700 1 $a Sridhar, Narasi. $3 3718583
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-56128-3
950 $a Chemistry and Materials Science (SpringerNature-11644)