Abstract

Info

Title:: Eliminating temerature influences in structural damage detection by using neural network

Author(s):: GU Jian-feng; WU Xiao-guang; YAO Yu-ling; (School of Highway, Chang’an University, Xi’an 710064, Shaanxi, China)

Keywords:: bridge engineering; frequency-based damage detection; BP neural network; temperature fluctuation

Abstract:: In order to eliminate adverse influences of temperature fluctuation on damage detection, a method based on neural network and a novel detection technique was proposed. Taking finite element models of a benchmark grid structure as example, which was representative of short and medium-span bridges, this paper analyzed the influence of temperature variations and several damage scenarios on structural frequency to testify the validity and reliability of the proposed method. A backward propagation neural network (BPNN) was established to formulate the quantitative model of temperature and frequencies of the first ten vertical modes for the intact structure under varying temperatures. Then, structural frequencies were predicted by the BPNN under the conditions of different temperatures, and then prediction errors between the network outputs and the target frequencies were calculated to eliminate temperature effects. Subsequently, Euclidean norm of prediction errors was utilized as a novelty index, and the relative difference between average values of novelty index sequences of candidate structure and intact structure were adopted as an indicator to detect damage. The results show that the proposed method is capable of ascertaining whether damage has occurred reliably and discriminate the general damage severity of the structure qualitatively regardless of varying temperatures. Additionally, this method has remarkable noise robustness.