Intelligent Data Driven Health Monitoring and Damage Detection of Concrete Bridge Girders using Hand-Held Mobile Devices
Start date: 03/01/15
End date: 02/28/17
- Kansas Department of Transportation
- Midwest Transportation Center
About the research
The need to repair, upgrade, or replace infrastructure bridge elements is increasing due to the aging of their materials and the expanding volume of traffic. Setting up bridge repair priorities is not a trivial task. This currently depends on the judgment of experienced inspectors. This approach is in fact very subjective since bridge health rating varies from one inspector to another.
Alternatively, this project intends to integrate the objectivity of computer simulations into the subject of predicting a consistent health index. This is expected to help bridge maintenance engineers and owners make realistic data-driven decisions about the relative health and damage of bridges to address the question of which bridge to repair first.
In this study, the methodology comprises a two-stage process. The first stage involves the generation of tens of thousands of simulations of a simple girder to mimic all possible cracking scenarios involved while generating stiffness values of damaged and healthy girders at specific stiffness nodes. Then, stiffness values of the damaged girder are normalized by their healthy counterparts. By integrating these stiffness ratios along the span of the girder, an objective health index ranging from 0 to 100 percent is derived. The outcome of this stage of the process involves the establishment of a vast database that links the stiffness ratios generated to the varied cracking scenarios and cracking parameters. The second stage involves training an artificial neural network (ANN) model for pattern recognition. Once trained, the neural network will be used to predict the stiffness ratios from the cracking parameters determined by visual inspection to yield a single objective health index, which will be used to compare different damaged bridge girders. Once the ANN model is trained, it will be loaded on tablets and tested in the field against the subjective visual evaluation of three different inspectors.