Rapid Damage Assessment in Infrastructure Systems using Vibration Measurements within a Machine Learning Framework

The primary goal of this proposal is to develop different Machine Learning (ML) algorithms for the rapid identification of damage in bridge structures using the bridge’s dynamic response during regular service operation. These algorithms are in theory applicable to any dynamical system but will be tailored specifically for bridge structures. This research will provide diagnostic tools that can be directly used in real time by bridge owners for rapid damage assessment. By collecting data and analyzing them in near real time, the algorithms should be able to provide information on the conditions of the structure and this could help in (1) controlling the traffic operation on the bridge as well as (2) in prioritizing resources in terms of rehabilitation/maintenance operations. The intended outcome of the project is the development of Machine Learning based tools for rapid damage assessment in bridges which are expected to have tremendous implications in practice and education of modern civil engineers.

• Record URL:
  https://cait.rutgers.edu/research/rapid-damage-assessment-in-infrastructure-systems-using-vibration-measurements-within-a-machine-learning-framework/

Language

• English

Project

• Status: Active
• Funding: $240000
• Contract Numbers:

  69A3551847102

  CAIT-UTC-REG74

• 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:

  Center for Advanced Infrastructure and Transportation

  Rutgers University
  100 Brett Road
  Piscataway, NJ  United States  08854-8058
• Project Managers:

  Szary, Patrick

• Performing Organizations:

  Columbia University

  610 SW Mudd
  500W 120th Street
  New York, New York  United States  10027
• Principal Investigators:

  Betti, Raimondo

  Brugger, Adrian

• Start Date: 20220901
• Expected Completion Date: 20230831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bridges; Machine learning; Repairing; Structural health monitoring; Vibration
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01867738
• Record Type: Research project
• Source Agency: Center for Advanced Infrastructure and Transportation
• Contract Numbers: 69A3551847102, CAIT-UTC-REG74
• Files: UTC, RIP
• Created Date: Dec 16 2022 12:23PM