Big Data-­Driven Prediction of Long-Term Bridge Performance and Management Improvements

Various structural and environmental factors and their interactions result in damage to state bridges. Thus, predicting long-term bridge performance and determining cost-effective management strategies for them have been daunting challenges. To effectively manage more than 600,000 bridges nationwide, the bridge structural health monitoring (SHM) field has shown remarkable advancements. By virtue of advanced sensing technologies, various real-time, high-precision data (such as strain and temperature data) for bridges are being collected over time and are now available. Still, a significant gap exists between new bridge big data and the tools and methods for management and prediction. The objective of this project is to establish a novel data processing framework. Through this framework, data from various sources can be seamlessly processed, reduced, and transformed, thereby producing tangible engineering information that promises to improve the accuracy of long-term bridge performance prediction and management plans in the future. Importantly, this project seeks to establish a sustainable and expandable framework that can fully leverage old, present, and even future bridge big data.

Language

  • English

Project

  • Status: Active
  • Funding: $160000
  • Contract Numbers:

    DTRT13-G-UTC37

  • Sponsor Organizations:

    Midwest Transportation Center

    Iowa State University
    2711 S Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664

    Office of the Assistant Secretary for Research and Technology

    Washington DC,   United States  20590

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664

    Iowa State University

    Bridge Engineering Center
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664

    Iowa State University

    Civil, Construction, and Environmental Engineering
    394 Town Engineering
    Ames, Iowa  United States  50011
  • Managing Organizations:

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664
  • Performing Organizations:

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664
  • Principal Investigators:

    Cho, In-Ho

    Shafei, Behrouz

    Alipour, Alice

    Phares, Brent

    Laflamme, Simon

    Chen, An

  • Start Date: 20160901
  • Expected Completion Date: 20171130
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01618655
  • Record Type: Research project
  • Source Agency: Midwest Transportation Center
  • Contract Numbers: DTRT13-G-UTC37
  • Files: UTC, RiP
  • Created Date: Dec 2 2016 11:18AM