Structural Health Monitoring Using Genetic Fuzzy Systems [electronic resource] / by Prashant M. Pawar, Ranjan Ganguli.
Contributor(s): Ganguli, Ranjan | [author.] | SpringerLink (Online service).Material type: BookDescription: VIII, 130p. 39 illus., 1 illus. in color. online resource.ISBN: 9780857299079 99780857299079.Subject(s): Engineering | ASTRONAUTICS | Engineering | STRUCTURAL MECHANICS | APPL.MATHEMATICS./ COMPUTATIONAL METHODS OF ENGINNEERING | ENGINEERING MATHEMATICS | MECHANICAL ENGINEERING | SIGNAL, IMAGE AND SPEECH PROCESSING | CIVIL ENGINEERING | CIVIL ENGINEERING | BUIDING REPAIR AND MAINTENANCE | BUILDING CONSTRUCTION | AEROSPACE TECHNOLOGY AND ASTRONAUTICSDDC classification: 620.1 Online resources: ir a documento
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|DOCUMENTOS DIGITALES||Biblioteca Jorge Álvarez Lleras||Digital||620.1 223 (Browse shelf)||Ej. 1||1||Available||D000722|
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.