Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain

Abstract

This paper presents a novel two stage improved Radial basis function (RBF) neural network for the damage identification of multimember structures in the frequency domain. The improvement of the proposed RBF network is carried out in two stages, viz. (i) first stage damage prediction by conventional RBF network trained with effective input-output patterns and (ii) in the second stage, minimization of the prediction error below the predefined error tolerance (3%) by training the network with patterns from reduced search space located after the first stage prediction. The network effective input patterns are fractional frequency change ratios (FFCs) and damage signature indices (DSIs), and the corresponding output patterns are stiffness values or damage severity of the structure at different damage levels. A Latin hypercube search (LHS) technique is used for finding the effective input-output patterns from the search space to improve the training efficiency. The numerical simulation of structural damage identification for two multimember structures; a six-storey steel structure and a nine-member frame structure, are evaluated with and without addition of 5% random noise to the input patterns using the proposed network. The novel improved RBF network is shown to be a good damage identification strategy for multiple member structures compared to conventional RBF and existing hybrid methods in terms of accuracy and computational effort.