Skip to Main Content

Bridge Modal Identification Via Video Processing & Quantification of Uncertainties

PI: Zhu Mao
Institution: University of Massachusetts Lowell
PI Email: Zhu_Mao@uml.edu
PI Phone: 978-934-5937

Background / Need:

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.

Graphic illustration for Bridge Modal Identification Via Video Processing & Quantification of Uncertainties. Fig. 1 demonstrate 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.

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.

Objectives:

The objectives of this project are to:

Graphic illustration for Bridge Modal Identification Via Video Processing & Quantification of Uncertainties. 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.

Fig. 2. Statistical modeling and decision-making for damages identification

  • Investigate the non-contact image/video processing techniques for global bridge dynamics evaluation, and modal-based properties will be extracted from the pixel data.
  • Computer vision algorithms will be applied to process the acquired data, and uncertainties, both aleatory and epistemic, will be quantified. With statistical modeling and classification, structural deficiency will be detected and evaluated with statistical significance.
  • Investigate the feasibility of the video-processing techniques in dealing with influence of environmental and traffic variabilities on bridges.
  • Assistant Professor Zhu Mao, Ph.D., Mechanical Engineering