2019 CSCE Annual Conference - Laval (Greater Montreal) Conference
Dr. Ayan Sadhu, Western University
Most of the existing infrastructure in North America was built in the post-World War II era. Many water and sewer treatment facilities, bridges, dams, wind turbines, culverts or pipelines are close to the end of their remaining useful life due to aging, growing populations and exponentially increasing operational load, extreme weather conditions and natural disasters. The ability to continuously monitor the desired functionality and integrity of civil infrastructure facilitates potential solutions to reduce annualized maintenance cost, while providing increased safety to the public. In the absence of adequate repair and maintenance, the progressive damage leads to the collapse of structures. Structural Health Monitoring (SHM) is an emerging and powerful diagnostic tool for damage detection and disaster mitigation of structures. The efficient diagnosis and prognosis of civil infrastructure require real-time assessment of its progressive damage over time under its in-service conditions.
Wavelet transform (WT) is a numerical tool that can decompose a temporal signal into a summation of time-domain functions of various frequency resolutions. The simultaneous time-frequency decomposition gives the WT a unique advantage over the traditional Fourier transform in analyzing nonstationary signals. One drawback of the WT is that its resolution is rather weak in the high-frequency region. Since structural damage is typically a local phenomenon captured most likely by high-frequency modes, this potential drawback can affect the application of the wavelet-based damage assessment techniques. Furthermore, most of the studies are restricted to identification of discrete damage only and there has been a limited study on progressive damage. The goal of this paper is to develop a robust damage detection algorithm capable of capturing damage progression using vibration data collected through various sensors. In this study, synchrosqueezing transform (SST) is used for progressive damage assessment in the structures with the aid of changes in modal parameters. Damage introduces localized singularities in the response signal of the structure; the wavelet transform can capture time-frequency information and analyze a localized portion of a signal. The results are validated using both numerical simulations and experimental studies.