Drive-by damage detection based on the use of CWT and sparse autoencoder applied to steel truss railway bridge

Structural ageing and material deterioration require infrastructure managers to continuously seek for improved solutions for bridge condition management. In the last two decades, vehicle-assisted bridge monitoring has emerged among researchers and engineers as a promising tool to support visual inspections, being a cost-efficient alternative to direct Structural Health Monitoring systems. In this work, the authors present a sparse-autoencoder-based damage detection methodology which exploits the vertical acceleration of train’s leading bogie to assess bridge health condition. The bridge under analysis in this work is a Warren truss bridge, whose FE model was designed based on the technical drawings of an actual structure, which belongs to the Italian railway line, and then validated through dynamic measurements. Raw bogie vertical accelerations are preprocessed through Continuous Wavelet Transform, allowing for the extraction of a specific frequency region of interest, governed by the modular configuration of the bridge as well as the forward speed of the convoy. Starting from the average curve of the computed wavelet coefficients, bridge health status is assessed through the use of a sparse autoencoder exploiting multiple train transits and two different damage indices. First of all the Hotelling’s statistic computed at the latent space level, and, secondly, the batch mean absolute reconstruction error. Different damage scenarios and intensities are tested in this work, considering the partial failure of stringers or cross-girder members due to corrosion, modelled as material mass loss and stiffness reduction. A large set of simulations allowed for testing the robustness of the methodology against operational variables, such as travelling speed, geometrical track irregularity evolution, and different weights of the convoy. Globally, promising damage detection performances were obtained when considering batches of 40 trains, even in presence of measurement noise and speed estimation inaccuracies.