Vibration-Based Damage Identification in Steel Girder Bridges Using Artificial Neural Network Under Noisy Conditions

Abstract

The vibration-based damage identification techniques use changes in modal properties of structures to detect damages. However, the results of these methods are not reliable under noise. Therefore, it is essential to clarify which method performs vigorous under noisy conditions. In this study, three damage detection methods, called modal strain energy-based damage index, modal flexibility, and modal curvature, are considered to detect damage with and without the presence of noise. The feasibility of these methods is demonstrated by applying a range of damage scenarios in the validated FE model of the I-40 Bridge. The info of the only first three bending mode shapes of the bridge is used to calculate damage indices. The outcome showed while all three methods were capable of detecting damage in the absence of noise, only the modal flexibility method could locate damages in the presence of noise. Thus, an approach is proposed to eliminate noise and quantify damage magnitude using an artificial neural network (ANN) and modal flexibility method. The modal flexibility damage index of different damage severities was contaminated with various noise levels used as input parameters to train the ANN. Results indicate the adequate performance of the trained ANN in noise-canceling and damage magnitude estimation.