Data-Driven Risk-Informed Bridge Asset Management and Prioritization Across Transportation Networks (RR-2)
A transportation network comprises hundreds to thousands of assets, each with a varying combination of design characteristics, ages, conditions, repair histories, and hazard exposures. With typically limited resources to both inspect and repair these assets, an approach to efficiently and effectively distribute the resources to ensure reliability and resilience of the network is needed. At the same time, inspection data is increasing in type, amount, and capability to assess structural states. This data includes information collected from new robotic technologies developed through the Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center (INSPIRE UTC). This project seeks to utilize the inspection data to assess assets across a transportation network to more effectively manage and prioritize resources across the network. It is a natural extension to the network of bridges from the risk analysis of individual bridges using localized inspection data for corrosion or scour evaluation. Approach and Methodology: This project will create a framework to map inspection data from individual bridges to assets across a transportation network. One of the challenges in doing this is the uncertainty in bridge conditions, aging processes, loadings, and predictions of performance across varying bridges. A risk-informed approach that considers varying characteristics across assets will therefore be implemented. Probabilistic inferences across a network will be made based on inspection data characteristics and similarity of parameters between assets, considering uncertainties in the inspection data, structural parameters, and environmental characteristics. Overall Objectives: This project aims to develop innovative ways to use inspection data and inform decisions for maintenance, repair, rehabilitation, or replacement actions at the infrastructure network scale. To inform these decisions, quantitative assessments and comparisons of estimated and predicted performance of bridges across a network must be made. This project will create a framework that takes input inspection data and infers risk for assets across a network to support bridge asset management and prioritization. Scope of Work in Year 1: (1) Create classes of bridges based on collected inspection data, (2) Define measures of similarity across bridges, and (3) Create a probabilistic mapping procedure to infer states of multiple assets based on individually-collected bridge inspection data.
Language
- English
Project
- Status: Active
- Funding: $506205
-
Contract Numbers:
69A3551747126
-
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:
Missouri University of Science and Technology
Rolla, MO United States 65409 -
Performing Organizations:
Georgia Institute of Technology, Atlanta
790 Atlantic Drive
Atlanta, GA United States 30332-0355 -
Principal Investigators:
Tien, Iris
- Start Date: 20200101
- Expected Completion Date: 20240630
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Asset management; Bridge management systems; Data analysis; Inspection; Risk analysis; Strategic planning
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;
Filing Info
- Accession Number: 01751726
- Record Type: Research project
- Source Agency: Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center
- Contract Numbers: 69A3551747126
- Files: UTC, RIP
- Created Date: Sep 11 2020 1:13PM