Structure damage detection using neural network with multi-stage substructuring

Abstract

Artificial neural network (ANN) method has been proven feasible by many researchers in detecting damage based on vibration parameters. However, the main drawback of ANN method is the requirement of enormous computational effort especially when complex structures with large degrees of freedom are involved. Consequently, almost all the previous works described in the literature limited the structural members to a small number of large elements in the ANN model which resulted ANN model being insensitive to local damage. This study presents an approach to detect small structural damage using ANN method with progressive substructure zooming. It uses the substructure technique together with a multi-stage ANN models to detect the location and extent of the damage. Modal parameters such as frequencies and mode shapes are used as input to ANN. To demonstrate the effectiveness of this approach, a two-span continuous concrete slab structure and a three-storey portal frame are used as examples. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations in the structures. The results show that this technique successfully detects all the simulated damages in the structure.