Accurate and Scalable Bridge Health Monitoring Using Drive-by Vehicle Vibrations
Graduate Researcher: Jingxiao Liu
Faculty Advisor: Hae Young Noh
The objective of my thesis work is to model, integrate, and generalize features extracted from drive-by vehicle dynamic responses to achieve accurate and scalable bridge health monitoring (BHM). BHM allows us to diagnose bridge damage in the earlier stages, which is essential for preventing more severe damage and collapses that may lead to significant economic and human losses. Conventional BHM approaches require installing sensors directly on bridges, which are costly, inefficient, and hard to scale up. My research overcomes these limitations by using vehicle vibration data when the vehicle passes over the bridge to monitor bridge health. This drive-by BHM approach builds on the intuition that recorded vehicle vibrations contain information about the vehicle-bridge interaction (VBI) and thus can indirectly inform us of the dynamic characteristics of the bridge. It has various advantages, including that each vehicle can monitor multiple bridges (i.e., economical) and there is no need for on-site maintenance of sensors and equipment on bridges.
Carnegie Mellon University