PI: Zhu Mao
Institution: University of Massachusetts Lowell
PI Email: Zhu_Mao@uml.eduPI Phone: 978-934-5937
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.
Fig. 1. Overview of the computer vision approach for modal identification
Fig. 1 demonstrates the experimental modal analysis flow via stereo-cameras and the algorithm of phase-based motion extraction and amplification.
The non-contact data are employed for the nature of its full-field and easy implementation characteristics.
Statistical modeling will be deployed once the modal information is extract, and the uncertainty will be studied via data-driven modeling and Bayesian inference, as demonstrated in Fig. 2.
The objectives of this project are to:
Fig. 2. Statistical modeling and decision-making for damages identification
Assistant Professor Zhu Mao, Ph.D., Mechanical Engineering