Hierarchical use of neural techniques in structural damage recognition

This study explores the possibility of using neural techniques to detect the presence of structural faults from dynamic response data in simple structures of practical interest for structural engineering. Some techniques of structural response signal processing and analysis devised for the purpose of improving the diagnostic capabilities of connectivistic methods are proposed through numerical experiments. To this end, cross-correlations between dynamic response signals are used as inputs for hierarchically organized networks which are able to assess and locate structural damage in numerical models. The satisfactory results obtained are discussed and explained with the aid of projections of the multi-dimensional decision space through the use of simple 'grow and learn' neural models.