Preserving Coastal Infrastructure through the Design and Implementation of Image-Based Structural Health Monitoring (iSHM)

 

 

 

 

 

Camera configuration a) diagram of optical setup and b) field of view, speckle pattern and subsets

It is estimated the average age of bridges in the U.S. is approaching 45 years, suggesting many of these structures may be in a state of disrepair and perhaps even reaching the end of their functional lives. In addition to age-related deterioration, these structures are exposed to weather and environmental hazards, further affecting their longevity.  Take, for example, high-profile infrastructure systems in the Hampton Roads region in Virginia. The Hampton Road Bridge Tunnel and the Chesapeake Bay Bridge are vulnerable to extreme weather events such as hurricanes, sea level rise, exposure to salting during snowstorms and underside exposure to saltwater spray. These environmental hazards cause corrosion and eventually cracking which impact the long-term performance of the structures.

To assess infrastructure for maintenance and repair, structural health monitoring (SHM) is an assessment strategy undertaken to determine the location, severity, and progression of damage. SHM is actually not used frequently and is primarily deployed on high-profile structural systems. Devin Harris, PhD, Associate Professor in the Department of Civil and Environmental Engineering at the University of Virginia, is tackling the problem with a fresh approach.

Harris believes image-based structural health monitoring (iSHM) can be a powerful tool for assessing condition and structural behavior, leveraging vision-based sensing techniques for describing the operational behavior of structural systems. With MATS-UTC funding, he is evaluating the laboratory performance of the iSHM concept. Using standard structural shapes under variable boundary conditions, Harris subjects a representative steel beam to a series of loading configurations, simulating real-world stresses on the structure, with measurements captured using high-resolution cameras. As the beam deforms under the various loads, the contrasting pattern painted on the beam also deforms proportionally. During testing, the cameras capture images of the behavior of the beam as it strains, rotates, deflects or deforms, which are then translated into full-field measurements of these phenomena using a technique called digital image correlation (DIC). These measurement results are then used in a structural identification scheme to update uncertainties in a finite element model of the structural system, which in turn can provide a mechanism to describe structural response and performance under different scenarios.

Preliminary results are promising. The vision-based sensing approach demonstrates real potential for deployment in the field with easy application of painted patterns on existing structures and eventual remote placement of weather-resistant cameras.  Harris envisions that the technology could eventually be used for load testing for bridges all over the country. Next steps include further laboratory studies to refine the approach, extensions to field evaluation of existing structures, and finally the development of smart cameras that are easy to use by DOTs, enabling reliable data collection and analysis and providing a cost effective way to approach health monitoring in the field.

Harris may be contacted at dharris@virginia.edu.