Joint damage identification using Improved Radial Basis Function (IRBF) networks in frequency and time domain

In this paper, a novel two-stage Improved Radial Basis Function (IRBF) neural network technique is proposed to predict the joint damage of a fifty member frame structure with semi-rigid connections in both frequency and time domain. The effective input patterns as normalized design signature indices (NDSIs) in frequency domain and acceleration responses in time domain are simulated numerically from finite element analysis (FEA) by considering different levels of damage severity using Latin hypercube sampling (LHS) technique. The conventional RBF network is used in the first stage of IRBF network and in the second stage reduced search space moving technique is employed for accurate prediction with less than 3% error. The numerical simulation of the substructural joint damage identification of a fifty requency domain ime domain DSIs cceleration responses EA HS technique member frame structure with and without addition of 5% Gaussian random noise to the input patterns is presented and compared with conventional CPN–BPN hybrid method. The two-stage IRBF method is found to be superior in accuracy to conventional hybrid methods as well as to conventional RBF method. An important benefit of the proposed novel IRBF method is the significant reduction in the computational time with good accuracy of joint damage identification. mproved RBF neural networks