A vibration-based damage assessment method to deal with time varying loading of pre-stressed structures

Vibration-based damage assessment is a well-established research field and several examples of practical applications to civil, mechanical and aerospace engineering can be found worldwide. However, the damage-sensitive features commonly used to perform this task can be affected by varying environmental and operating conditions. As a consequence for the damage assessment, large variations of monitored features mask subtle effects due to damage, which remain undetected. Moreover, the modal identification used to extract damage-sensitive features faces constricting requirements in terms of signals stationarity and performance accuracy. This study is conceived to address both these issues by focusing on the non-stationarity of the loading conditions of tensioned structures, for instance cables and pre-stressed beams. The capability of spectral methods to deal with the modal identification of non-stationary systems is enhanced by a curve-fitting procedure based on nonlinear least squares optimisation. Wavelet analysis is applied for comparison and validation of the FFT-based technique. Identified natural frequencies are then used for the damage detection, exploiting the capacity of singular values decomposition (SVD) to discriminate between damage-related events and the non-stationarity of the structural response. A reduced order realization of the damage features set is performed to amplify changes not belonging to measurements variability but deriving from exogenous events, such as damage. The proposed methodology is validated by experimental analyses carried out on beams subjected to time-varying loading conditions in order to simulate the health monitoring of quasi and nonstationary systems. The innovative character of the research is represented by the adoption of the SVD to aid the detection of damage events that otherwise would be undetected. The results of the experimental tests confirm the efficacy of the SVD-based method to divorce the effects of exogenous events (long-term trend) from those related to damage. The proposed methodology outperforms current damage detection methods without requiring the definition of a reference set. Moreover, the robustness of the SVD to medium-level noise opens promising prospects for its adoption in the damage assessment of real-word time-varying structures.