Neural network based damage detection of dynamically loaded structures

Abstract

The aim of the paper is to describe a methodology of damage detection which is based on artificial neural networks in combination with stochastic analysis. The damage is defined as a stiffness reduction (bending or torsion) in certain part of a structure. The key stone of the method is feed-forward multilayer network. It is impossible to obtain appropriate training set for real structure in usage, therefore stochastic analysis using numerical model is carried out to get training set virtually. Due to possible time demanding nonlinear calculations the effective simulation Latin Hypercube Sampling is used here. The important part of identification process is proper selection of input information. In case of dynamically loaded structures their modal properties seem to be proper input information as those are not dependent on actual loading (traffic, wind, temperature). The methodology verification was carried out using laboratory beam. © 2009 Springer-Verlag.