Objetivo The basic problem of damage detection is to deduce the existence of a defect in a structure from measurements taken at sensors distributed on the structure. Especially in aeronautical structures, cracks, delaminations and debondings are typical types of damages often encountered. The problem is essentially one of pattern recognition. Artificial neural networks show considerable promise for damage diagnosis. In the most basic, supervised learning, approach to deriving a neural network, the network is presented with pairs of data vectors, the input being the vector of measurements from the system and the output being the desired damage classification. At each presentation of the data, the internal structure of the network is modified, in order to bring the actual network outputs into correspondence with the desired outputs. This iterative procedure is terminated when the network outputs have the required properties over the whole training set. In a structural application, the training data may be provided by finite element (FE) analysis. This has the advantage of allowing a large range of boundary conditions and static/dynamic load cases to be analysed. FE analysis may be a little unrealistic as there is no limit on the spatial resolution of the data which is obtained, e.g. strains. In reality, the number of sensors available will be limited and this will, of course, place restrictions on the resolution of data. As a result, it is necessary in practice to optimise the number and location of sensors for a given problem. The main objective of the current proposal is to develop a mathematical algorithm for optimal strain sensor (strain gauges, fiber Bragg grating or other) placement in aeronautical composite structures for maximum damage detectability. The mathematical method to be used will be a genetic algorithm based on neural networks. The genetic algorithm will be trained from finite element analyses simulating impact scenarios (damage initiation) and operational Ámbito científico natural sciencescomputer and information sciencesartificial intelligencemachine learningsupervised learningengineering and technologycivil engineeringstructural engineeringstructural health monitoringengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsnatural sciencescomputer and information sciencesartificial intelligencepattern recognitionnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programa(s) FP7-JTI - Specific Programme "Cooperation": Joint Technology Initiatives Tema(s) JTI-CS-2009-1-GRA-01-020 - Sensorised Composite Convocatoria de propuestas SP1-JTI-CS-2009-01 Consulte otros proyectos de esta convocatoria Régimen de financiación JTI-CS - Joint Technology Initiatives - Clean Sky Coordinador ETHNICON METSOVION POLYTECHNION Aportación de la UE € 20 249,00 Dirección HEROON POLYTECHNIOU 9 ZOGRAPHOU CAMPUS 157 80 ATHINA Grecia Ver en el mapa Región Αττική Aττική Κεντρικός Τομέας Αθηνών Tipo de actividad Higher or Secondary Education Establishments Contacto administrativo Georgios Tsamasphyros (Prof.) Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Coste total Sin datos Participantes (1) Ordenar alfabéticamente Ordenar por aportación de la UE Ampliar todo Contraer todo GMI AERO Francia Aportación de la UE € 2 250,00 Dirección 13 RUE GEORGES AURIC CAP 19 75019 Paris Ver en el mapa Región Ile-de-France Ile-de-France Paris Tipo de actividad Private for-profit entities (excluding Higher or Secondary Education Establishments) Contacto administrativo Roland Chemama (Mr.) Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Coste total Sin datos
