Project C3.2018: Condition Assessment of Corroded Prestressed Concrete Bridge Girders

Project Abstract:

The problem we are trying to solve is the condition assessment of corroded prestressed concrete (PC) bridge girders in New England. The problem is important because that PC bridge girders are a critical component of highway bridges. Concrete spalling and prestressing strand corrosion not only cause losses in prestress but also lead to premature failures of PC bridges. We propose to 1) conduct multiplysical field inspection (using 3D photogrammetry, radar, impact-echo, and ultrasound) and to 2) develop an integrated assessment framework for predicting the level of structural damage and prestress losses for PC bridge girders.

This project will enhance the transportation infrastructure durability as follows:
1) Efficient condition assessment algorithm – The chemical-mechanical model proposed by this research will provide a theoretical, but computationally efficient algorithm for assessing the remaining structural strength of PC bridge girders.
2) Data-driven decision-making – The vulnerability function for defining multiple damage states adopts the Bayesian framework, a data-driven method. The outcome can be customized improve the durability of PC bridges through effective structural repair.

Sponsors:
University Transportation Centers Program, Department of Transportation
University of Massachusetts Lowell
Western New England University

Implementation of Research Outcomes:
We have developed a method to control the level of steel rebar corrosion inside reinforced concrete cylinder specimens in order to subject them to a pull-out test.

Impacts and Benefits of Implementation:
This project will enhance the transportation infrastructure durability as
follows:
• Efficient condition assessment algorithm – The chemical-mechanical model proposed by this research will provide a theoretical, but computationally efficient algorithm for assessing the remaining structural
strength of PC bridge girders.
• Data-driven decision-making – The vulnerability function for defining multiple damage states adopts the Bayesian framework, a data-driven method. The outcome can be customized improve the durability of PC
bridges through effective structural repair.

Related Links:
https://www.uml.edu/Research/tidc/projects/assessment-corroded-prestressed-bridge-girders.aspx

Downloadable Documents