The objective of this research is to provide accurate, rapid, nearly continuous, and cost-effective assessments of a large population of bridges using the data collected from a set of vehicles equipped with sensors able to capture the dynamic interaction between the vehicles and the bridge. This grant provides funding for the development of a new approach for assessing the health of bridges that uses vehicles with on-board sensors to collect condition information about the bridges over which they travel. The dynamic characteristics of the bridge are affected by the damage in the form of cracks, corrosion, and frozen bearings. The first premise of this research is that these changes will be detectable from the dynamic responses collected from a large number of vehicles travelling over the bridge. A second premise is that the type, location and extent of the damage on the bridge will be classified. The new approach will use multiresolution (MR) signal processing and pattern recognition algorithms to detect and classify bridge damage.
If successful, the results of this research will be beneficial to bridge authorities by leading to the development of a new indirect assessment method for monitoring the health of a large number of bridges using the same instrumented vehicles. The method will have a significant economic impact by providing an efficient and more cost effective method to improve the management of the overall structural condition of bridges. The results of this research will also lead to new signal-processing algorithms that will capture signals collected from vehicles. In addition, knowledge gained during the project will be useful for determining the applicability of this approach to different types of structures. The experience and the insights provided by this research project, which will directly involve two PhD graduate students and a number of undergraduate students, will be transferred in courses and demonstrations both at Carnegie Mellon University and the University of Pittsburgh.
