Abstract Summary
Damage detection is the monitoring of the onset of damage in the structural system response, deviating from a reference undamaged state. The application of data-based Structural Health Monitoring (SHM) for damage detection is characterized by three fundamental aspects: the features extracted and selected as representative of damage in the structure, the metrics used as novelty or damage index and the model built to highlight underlying patterns indicative of the presence of damage. Focusing on the first step towards the optimal application of the data-based SHM approach, the extracted parameters should be truly sensitive to the presence of damage, robust to noise and distinguishable from the environmental and operational variability. Great research effort has been spent into the study of the sensitivity to damage of modal parameters. Numerous publications are available reporting requirements, potential and limits of parameters such as resonance frequencies, mode shapes, mode shapes curvature, modal flexibility, modal strain energy, etc. On the other hand, the sensitivity to damage of statistical parameters have also been investigated with the use of autoregressive models, performing damage detection directly on vibration data and avoiding the modal identification of the structure. This paper addresses a comparison between the use of modal and statistical parameters as damage sensitive features in a damage detection problem. This problem is approached as an outlier detection problem and the features used are evaluated based on the detection performance, the extraction/selection requirements, and the computational cost. In order to control all parameters that could affect the structural response, an artificial dataset is generated from the numerical model of a three-storey steel frame structure in different damage scenarios.
