Project 3.8: Bridge Modal Identification via Video Processing and Quantification of Uncertainties

Project Abstract:

Bridges form a critical category of the U.S. transportation infrastructure, yet the current structural condition is only evaluated at “C+” according to the 2017 ASCE Infrastructure Report Card. In addition to the fact that 9.1% of the bridges in U.S. are structurally deficient, the bridges in New England are especially experiencing the burden of busy traffic and harsh wintery weather. There is a variety of factors that may affect the bridge dynamics and deteriorate the structures, such as creeping, corrosion, cyclic thermal loadings and accidental damages, and identification modal properties provides a global evaluation capability with rich physical meaning. However, this complicated scenario brings up the demanding in conducting the heterogeneous data acquisition and in-situ modal analysis, as well as quantifying the enormous amount of uncertainties that may come across.
The problem we are trying to solve is to adopt portable video cameras and by processing the acquired videos, bridge dynamic systems, especially full-field mode shapes will be extracted to enhance the status awareness. The challenges exist while dealing with the rapidly changing environments and traffics, so that the statistical modeling is needed when interpreting the extracted information.

Sponsors:
University Transportation Centers Program, Department of Transportation
University of Massachusetts Lowell

Implementation of Research Outcomes:
The application of the non-contact optical sensing and motion technologies was used at the newly installed Grist Mill Bridge in Hampden, ME. Data was collected on site and processed in order to obtain dynamic information of the bridge.

Related Links:
https://www.uml.edu/Research/tidc/projects/bridge-modal-identification.aspx

Downloadable Documents