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 ...