Structural Reliability and Health Monitoring of Composite Structures with Artificial Intelligence
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Form:Einzelkauf Download
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Sprache:Englisch
180,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
29.06.2026
Verlag
Elsevier Science & Techn.Seitenzahl
350 (Printausgabe)
Dateigröße
48294 KB
Sprache
Englisch
EAN
9780443510953
Structural Reliability and Health Monitoring of Composite Structures with Artificial Intelligence offers a comprehensive review of the application of AI in the SHM of composite structures. Sections cover new developments such as the use of machine learning, deep learning, and artificial neural networks in SHM, while also illustrating the integration of non-destructive testing (NDT) methods with AI algorithms. The content structure follows logical progression: from basic material concepts, design, manufacturing and fabrication, through damage and failure modes, advanced SHM monitoring, probabilistic design concepts, and AI based schemes for SHM.In addition, a full chapter is also dedicated to applied case studies in pipelines, ducts, and plates) using analytical, numerical, and simulation tools (ANSYS, MATLAB). The book's key audience includes postgraduate students, academic researchers, and practicing engineers who are working in civil, mechanical, materials, and aerospace engineering.
- Provides comprehensive integration of the fundamentals of composite materials, SHM, NDT, and AI all in a single resource
- Includes detailed, practical case studies using real-world scenarios in pipelines, plates, and wind turbine blades
- Presents up-to-date content on machine learning, deep learning, and neural networks in the SHM context
- Contains comparative analysis of SHM/NDT methods and AI schemes
- Covers risk management and reliability assessment using AI and data fusion techniques
- Provides comprehensive integration of the fundamentals of composite materials, SHM, NDT, and AI all in a single resource
- Includes detailed, practical case studies using real-world scenarios in pipelines, plates, and wind turbine blades
- Presents up-to-date content on machine learning, deep learning, and neural networks in the SHM context
- Contains comparative analysis of SHM/NDT methods and AI schemes
- Covers risk management and reliability assessment using AI and data fusion techniques
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