Autor/Autorin: Sahin Melin
The author received his B.S. and M.S. from Department of Aerospace Engineering, Middle East Technical University, Turkey and obtained his Ph.D. from University of Southampton, UK. Dr. Sahin''s research interests are experimental analysis of vibrating structures, smart materials and structures, structural health monitoring and damage identification.
Laminated composites and sandwich structures are increasingly being used in aeronautical, marine and offshore applications where high stiffness and light weight are the primary issues. During their service life, these structures experience extreme loadings and harsh environmental conditions potentially leading to structural damage. In order to maintain the performance of the structure, localisation and quantification of the damage is a promising research area. Since the determination of the severity and the location of the damage is an inverse, non-linear and non-unique problem, an intelligent algorithm is generally needed for the analyses. This book presents a damage detection algorithm, which uses vibration-based analysis data obtained from beam-like structures to locate and quantify the damage via artificial neural networks (ANN). Multilayer feedforward backpropogation ANNs are designed, trained, validated and tested by using different damage scenarios obtained from finite element analyses. The results of the experimental verification depict that the damage can be evaluated by using damage sensitive global and local dynamic features measured from beam-like sensory structures.
The author received his B.S. and M.S. from Department of Aerospace Engineering, Middle East Technical University, Turkey and obtained his Ph.D. from University of Southampton, UK. Dr. Sahin's research interests are experimental analysis of vibrating structures, smart materials and structures, structural health monitoring and damage identification.
Klappentext
Laminated composites and sandwich structures are increasingly being used in aeronautical, marine and offshore applications where high stiffness and light weight are the primary issues. During their service life, these structures experience extreme loadings and harsh environmental conditions potentially leading to structural damage. In order to maintain the performance of the structure, localisation and quantification of the damage is a promising research area. Since the determination of the severity and the location of the damage is an inverse, non-linear and non-unique problem, an intelligent algorithm is generally needed for the analyses. This book presents a damage detection algorithm, which uses vibration-based analysis data obtained from beam-like structures to locate and quantify the damage via artificial neural networks (ANN). Multilayer feedforward backpropogation ANNs are designed, trained, validated and tested by using different damage scenarios obtained from finite element analyses. The results of the experimental verification depict that the damage can be evaluated by using damage sensitive global and local dynamic features measured from beam-like sensory structures.
