Data-Driven Risk-Informed Bridge Asset Management and Prioritization across Transportation Networks (RR-2)

Lead University:

Georgia Institute of Technology (GT)

Principal Investigator:

Dr. Iris Tien, GT

PI Contact Information:

Phone: (404) 894-8269  |  Email:

Funding Sources and Amounts Provided:
GT School of Civil and Environmental Engineering: $53,650
INSPIRE UTC: $107,301

Total Project Cost: $160,951

Match Agencies ID or Contract Number

GT: In-Kind Match |  INSPIRE UTC: 00055082-02D

INSPIRE Grant Award Number: 69A3551747126

Start Date: January 1, 2020
End Date: June 30, 2021

Brief Description of Research Project:

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 INSPIRE University Transportation Center. 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.

Implementation of Research Outcomes:
Research outcomes and implementation plan will be described towards the end of this project.

Impacts/Benefits of Implementation: 
Impact/benefits of implementation will be summarized at the end of this project.

Project Website:

Progress Reports: