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Structural Health Monitoring Using Genetic Fuzzy Systems [recurso electrónico] / by Prashant M. Pawar, Ranjan Ganguli.

Por: Pawar, Prashant M [author.]Colaborador(es): Ganguli, Ranjan [author.] | SpringerLink (Online service)Tipo de material: Texto

TextoEditor: London : Springer London : Imprint: Springer, 2011Descripción: VIII, 132 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9780857299079Tema(s): Engineering | Engineering mathematics | Mechanical engineering | Astronautics | Civil engineering | Building construction | Engineering | Structural Mechanics | Signal, Image and Speech Processing | Aerospace Technology and Astronautics | Building Repair and Maintenance | Civil Engineering | Appl.Mathematics/Computational Methods of EngineeringFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 620.1 Clasificación LoC:TA349-359Recursos en línea: Libro electrónico Texto

Contenidos:

Introduction -- Genetic Fuzzy Systems -- SHM of Beams -- SHM of Composite Beams -- SHM of Helicopter Rotors.

En: Springer eBooksResumen: Structural health monitoring (SHM) has emerged as a prominent research area in recent years owing to increasing concerns about structural safety, and the need to monitor and extend the lives of existing structures. Structural Health Monitoring Using Genetic Fuzzy Systems elaborates the process of intelligent SHM development and implementation using the evolutionary system. The use of a genetic algorithm automates the development of the fuzzy system, and makes the method easy to use for problems involving a large number of measurements, damage locations and sizes; such problems being typical of SHM. The ideas behind fuzzy logic, genetic algorithms and genetic fuzzy systems are also explained. The functionality of the genetic fuzzy system architecture is elucidated within a case-study framework, covering: • SHM of beams; • SHM of composite tubes; and • SHM of helicopter rotor blades. Structural Health Monitoring Using Genetic Fuzzy Systems will be useful for aerospace, civil and mechanical engineers working with structures and structural components. It will also be useful for computer scientists and applied mathematicians interested in the application of genetic fuzzy systems to engineering problems.

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Notas de título ( 3 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras
Libro Electrónico	Biblioteca Electrónica	Colección de Libros Electrónicos	TA349 -359 (Browse shelf(Abre debajo))	1	No para préstamo		370652-2001

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Previo	TA347 .B69 The boundary element method for plate analysis	TA347 .F5 Finite element method	TA347 .F5 The finite element method	TA349 -359 Structural Health Monitoring Using Genetic Fuzzy Systems	TA349 -359 Structural and Failure Mechanics of Sandwich Composites	TA349 -359 Anisotropic Elastic Plates	TA349 -359 Computational Methods in Elasticity and Plasticity	Siguiente

Introduction -- Genetic Fuzzy Systems -- SHM of Beams -- SHM of Composite Beams -- SHM of Helicopter Rotors.

Structural health monitoring (SHM) has emerged as a prominent research area in recent years owing to increasing concerns about structural safety, and the need to monitor and extend the lives of existing structures. Structural Health Monitoring Using Genetic Fuzzy Systems elaborates the process of intelligent SHM development and implementation using the evolutionary system. The use of a genetic algorithm automates the development of the fuzzy system, and makes the method easy to use for problems involving a large number of measurements, damage locations and sizes; such problems being typical of SHM. The ideas behind fuzzy logic, genetic algorithms and genetic fuzzy systems are also explained. The functionality of the genetic fuzzy system architecture is elucidated within a case-study framework, covering: • SHM of beams; • SHM of composite tubes; and • SHM of helicopter rotor blades. Structural Health Monitoring Using Genetic Fuzzy Systems will be useful for aerospace, civil and mechanical engineers working with structures and structural components. It will also be useful for computer scientists and applied mathematicians interested in the application of genetic fuzzy systems to engineering problems.