Bayesian Network Modeling of Corrosion - Rilegato

Rilegato

ISBN 10: 3031561279 ISBN 13: 9783031561276

Casa editrice: Springer Nature, 2024

Riassunto

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.

Su questo libro

Informazioni sull'autore

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.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031561279
• ISBN 13: 9783031561276
• Rilegatura: Copertina rigida
• Numero di pagine: 352
• Redattore: Sridhar Narasi
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Germania