Bayesian Network Modeling of Corrosion

Narasi Sridhar

ISBN 10: 3031561279 / ISBN 13: 9783031561276

Published by Springer, Springer Jun 2024, 2024

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 352 pp. Englisch.

Seller Inventory # 9783031561276

Bibliographic Details

• Title: Bayesian Network Modeling of Corrosion
• Publisher: Springer, Springer Jun 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 3031561279
• ISBN 13: 9783031561276
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 24D millimeters 241 millimeters width by 160 millimeters height by 24 millimeters depth
• Weight: 757 grams

About this title

Synopsis

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.

About the Author

Dr. Narasi Sridhar is CEO of MC Consult LLC, in Temecula, CA and an Adjunct Professor in the Department of Materials Science & Engineering, The Ohio State University. He has over 45 years of experience in corrosion science and engineering.