Structures as Sensors: Inferring Itself

As part of the “itself” aspect of the structures as sensors idea, we have been working on structural health monitoring (SHM) using wireless sensing units and statistical pattern recognition. These systems allow automated monitoring of structural conditions (self-aware) in an efficient and reliable way to reduce maintenance costs and prevent catastrophic failure. Specifically, we have been working on vibration based damage diagnosis algorithm using wavelet analysis, time-series modeling, and change point detection.

We have been investigating practical challenges in SHM, such as sparse sensing, noisy and nonstationary measurements, online decision making, and varying environmental and operational conditions. My group has also been working on uncertainty modeling and information updating that are necessary to incorporate SHM sensor data to traditional analytical seismic risk analysis approaches, in order to improve the loss estimation of civil structures over their lifetime and allow them to be better prepared for extreme events.