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Development of a Structural Health Monitoring System to Evaluate Structural Capacity and Estimate Remaining Service Life for Bridges

Project Details
STATUS

Completed

PROJECT NUMBER

10-367, TPF-5(219)

START DATE

03/01/10

END DATE

01/22/20

FOCUS AREAS

Infrastructure

RESEARCH CENTERS InTrans, BEC
SPONSORS

Federal Highway Administration Transportation Pooled Fund
Iowa Department of Transportation

PARTNERS

SHM Pooled Fund

Researchers
Principal Investigator
Brent Phares

Bridge Research Engineer, BEC

About the research

Bridges constitute the most expensive assets, by mile, for transportation agencies around the US and the world. Most of the bridges in the US were constructed between the 1950s and the 1970s. Consequently, an increasing number of bridges are getting old and requiring much more frequent inspections, repairs, or rehabilitations to keep them safe and functional. However, due to constrained construction and maintenance budgets, bridge owners are faced with the difficult task of balancing the condition of their bridges with the cost of maintaining them.

Bridge maintenance strategies depend upon information used to estimate future condition and remaining life of bridges. The desire of many departments of transportation (DOTs) is to augment their existing inspection process and maintenance system with a system that can objectively and more accurately quantify the state of bridge health in terms of condition and performance, aid in inspection and maintenance activities, and estimate the remaining life of their bridge inventory in real time. To better manage bridge inventories, tools that can accurately predict the future condition of a bridge, as well as its remaining life, are required.

One of the key requirements for an effective infrastructure management system is the establishment of a structural health monitoring (SHM) system An SHM system traditionally consists of a network of monitoring sensors, data acquisition, and communication hardware and software capable of carrying out bridge condition assessments in real-time and accurately and objectively predicting the health of the infrastructure components and systems.

For this project, the research team developed an automated SHM system that could detect bridge damage and estimate load ratings of bridges, as well as models to develop predictions for future condition ratings of bridges. The SHM system and models were then used to develop a bridge maintenance prioritization system for DOTs to augment current bridge management practices.

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