Abstract
There has been a significant increase in the use of prestressed concrete as a building material. In 2009 and 2010, prestressed concrete bridges composed 44% of all newly built and replaced bridges in the US. With the increased need for structural health monitoring (SHM) for bridge assessment and the increased use of prestressed concrete for building bridges, the need for the identification of monitoring parameters and the development of damage detection algorithms specific to prestressed concrete increased. In this paper, the parameters evaluated are the prestressing force value at transfer of the prestressing force and the width of early-age cracks. Transfer of the prestressing force in accordance with design values is of extreme importance because improper transfer can result in failure or malfunction at loads lower than predicted by designers. Early-age cracks can develop in prestressed concrete bridges prior to the transfer of the prestressing force due to shrinkage and thermal stresses. Partial or full closure of the cracks can occur during transfer of the force. Monitoring of the residual crack opening is important in order to ensure continued structural integrity or predict reduced structural capacity. This paper outlines two created methods that use a statistical approach for monitoring prestressing forces and the condition of early-age cracks by accounting for uncertainties and setting thresholds. The methods have been validated through application to two real-life structures, the main span and the southeast leg of Streicker Bridge on the Princeton University campus, and comparison to design values.
Original language | English (US) |
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State | Published - 2015 |
Event | 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015 - Torino, Italy Duration: Jul 1 2015 → Jul 3 2015 |
Other
Other | 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015 |
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Country/Territory | Italy |
City | Torino |
Period | 7/1/15 → 7/3/15 |
All Science Journal Classification (ASJC) codes
- Building and Construction
- Civil and Structural Engineering
- Artificial Intelligence