Substructural identification using neural networks

In relation to the problems of damage detection and safety assessment of existing structures, the estimation of the element-level stiffness parameters becomes an important issue. This study presents a method for estimating the stiffness parameters of a complex structural system by using a backpropagation neural network. Several techniques are employed to overcome the issues associated with many unknown parameters in a large structural system. They are the substructural identification and the submatrix scaling factor. The natural frequencies and mode shapes are used as input patterns to the neural network for effective element-level identification particularly for the case with incomplete measurements of the mode shapes. The Latin hypercube sampling and the component mode synthesis methods are adapted for efficient generation of the patterns for training the neural network. Noise injection technique is also employed during the learning process to reduce the deterioration of the estimation accuracy due to measurement errors. Two numerical example analyses on a truss and a frame structures are presented to demonstrate the effectiveness of the present method.