Descriptive and Predictive Deep Learning Analytical Tools for Enhanced Bridge Management: Bridge Subtyping and Bridge Deterioration Forecasting

Several U.S. Acts mandate the government to perform regular bridge inspections in order to assure health of the national bridge network. These inspections have been conducted since the 1970s and have generated valuable historic databases of bridge data based in local and state governmental agencies across the nation. While these agencies currently use the inspection data to maintain bridges and prevent failure, the research team believes the inspection databases are heavily underutilized. In particular, with the advent of machine learning and data mining methods, the team envisions data-driven solutions that can derive much more valued hidden knowledge from these databases that can be utilized to significantly enhance bridge management. While in the past various data-driven deterioration models are proposed in the literature to model bridge deterioration, these models either suffer from low accuracy or are too complex to be applicable. Moreover, they only address the problem of deterioration forecasting. Recently deep learning is shown to significantly outperform other analytical modeling methodologies in a variety of application domains. In this study, the team proposes to develop two analytical tools based on deep learning models for enhanced bridge management. The proposed tools will address the problems of bridge subtyping (for descriptive analysis of inspection data to effectively categorize bridges to groups based on their performance characteristics and behavioral trends), and bridge deterioration forecasting (for predictive analysis of the bridge data to accurately identify quantitative descriptors for the structure deterioration state (e.g., condition ratings) as well as any possible anomalies in the deterioration pattern of the bridge structure).

Language

  • English

Project

  • Status: Active
  • Funding: $80000
  • Contract Numbers:

    69A3551747108

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Mountain-Plains Consortium

    North Dakota State University
    P.O. Box 6050, Department 2880
    Fargo, ND  United States  58108-6050
  • Project Managers:

    Tolliver, Denver

  • Performing Organizations:

    Dept. of Computer Science and Engineering

    University of Colorado Denver
    Denver, CO  United States 

    Dept. of Civil Engineering

    University of Colorado Denver
    Denver, CO  United States 
  • Principal Investigators:

    Banaei-Kashani, Farnoush

    Nogueira, Carnot

  • Start Date: 20200218
  • Expected Completion Date: 20220731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Source Data: MPC-616

Subject/Index Terms

Filing Info

  • Accession Number: 01732637
  • Record Type: Research project
  • Source Agency: Mountain-Plains Consortium
  • Contract Numbers: 69A3551747108
  • Files: UTC, RiP
  • Created Date: Mar 2 2020 10:56AM