Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted the vehicle rather than bridge to be monitored, with clear advantages in terms cost and flexibility. This work aims at further exploring feasibility effectiveness novel tools for indirect health railway structures, by introducing higher level accuracy damage modelling, achieve more close-to-reality results. A numerical study is carried out means FE 3D model short span Warren truss bridge, simulating dynamic interaction bridge/track/train structure. Two kinds defects are simulated, first one affecting connection between lower chord side diagonal member, second involving joint cross-girder chord. Accelerations gathered from train bogie different working conditions intensities analyzed through two time-frequency algorithms, namely Continuous Wavelet Huang-Hilbert transforms, evaluate their robustness disturbing factors. Compared previous studies, complete rail vehicle, together structural scheme place 2D equivalent widely adopted literature, allow detailed realistic representation effects dynamics. Good results obtained both algorithms case time-invariant track profile, whereas Transform found robust when deterioration irregularity simulated.