In vibration-based structural health monitoring the natural frequencies of the monitored structures are subjected to different sources of change including: (i) varying environmental conditions (i.e., temperature, humidity, and wind conditions) and (ii) structural degradation and damage. Thus, an accurate detection of structural degradation and damage depends on removing any influence from the environmental conditions on the natural frequencies. If such an influence is not removed, there is a risk of false positive or negative damage diagnosis and thus the damage detection is not robust and reliable. In this study, removal of the environmental conditions and the following damage detection is conducted by applying an output-only principal component analysis as well as an inputoutput multi linear regression model. The purpose of this is to assess robustness of these methods by highlighting their advantages and disadvantages in terms of modeling the environmental conditions and detecting structural changes. The investigation is based on vibration data of a wooden mast structure subjected to natural loads and induced with damage at different levels.
|Number of pages||11|
|Publication status||Published - 2020|
|Event||IMAC XXXVIII: Space Technologies for Humanity - Hyatt Regency Houston, Houston, United States|
Duration: 10 Feb 2020 → 13 Feb 2020
Conference number: 38
|Location||Hyatt Regency Houston|
|Period||10/02/2020 → 13/02/2020|
- Vibration-based monitoring
- Natural frequencies
- Environmental effects
- Damage detection
Andersen, P. L., D.R. Amador, S., Nielsen, S. T., Katsanos, E., & Brincker, R. (2020). Vibration-based Damage Detection Using Input-output and Output-only Environmental Models: A Comparison. Paper presented at IMAC XXXVIII, Houston, United States.