Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders

Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners, in the attempt to make bridge condition-based monitoring more cost-efficient. In this work, the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status. To do so, continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span. According to authors’ best knowledge, this is the first case in which an unsupervised technique, which relies on the use of sparse autoencoders, is used to localize damages. The bridge considered in this work is a Warren steel truss bridge, whose finite element model is referred to an actual structure, belonging to the Italian railway line. To investigate damage detection and localization performances, different operational variables are accounted for: train weight, forward speed and track irregularity evolution in time. Two configurations for the virtual measuring channels were investigated: as a result, better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal.