Research Thrust on Infrastructure Integrity Monitoring and Condition Assessment
Wireless Bridge Health Monitoring – Based on recently completed research projects “Survey and Investigation of the State-of-the-Art Remote Wireless Bridge Monitoring System” and several bridge load tests, the BEST Center team led by Drs. C. C. Fu and Y. Zhang incorporating with NCSU on developing a more advanced and durable system for bridge monitoring. Pure Technology provides their expertise on remote monitoring device on cable structures, such as Bay Bridge.
Water/Waste Water Piping Monitoring and Assessment – Recent contacts and meetings with Pure Technology, Inc. USA, have developed into a non-disclosure agreement on exchange information on the state-of-the-art technology used in the water/waste water piping monitoring and assessment. Meetings and presentations were made to the City of Baltimore,Baltimore County, WSSC and WASA by Pure and UMD, respectively on using this new technology on their facilities and systems. This is a thrust area built on the complemented strength and expertise of both teams for water/waste water piping monitoring and assessment. Researchers at NCSU are also contributing to this research thrust by verifying their ambient noise based damage detection algorithms on round steel pipes.
New Sensor Development – Various sensors used in the past bridge load tests are all off-the-shelf and commercially available. It is good in collecting information through strains, temperature, humidity or resistance, but not in seeking structural health information. Drs. Zhang and Fu have worked on the development of a piezo paint-based acoustic emission sensor for bridge prognosis, ideal for the close-range monitoring of local structural hot spot areas with curved surfaces and complex geometries.
Concrete and Steel Condition Prognosis and Decision Making – Detection and diagnosis of damage only mark the early stages of component failure, such as a fatigue failure of sliding plates on Tydings Bridge; prognosis is the capability to incorporate the diagnostic information to predict the future progression of damaged state of the structural components to failure under environmental conditions. Bayesian Network methodology is currently in development by Drs. Baecher, Fu and Zhang, which allows real-time, complex measurement data to be processed to draw logically consistent parameter inference, which in turn can serve as input to prognosis models.
Visit the project website at: www.ncrst.umd.edu