Framework for In-service Bridge Health Assessment using Quantitative Measures

With nearly one quarter of today's highway bridges classified as "deficient", it is ever more important to quantify the safe level of performance using current and newly developed in-situ condition assessment techniques. This work aims at developing additional performance assessment tools to be used in conjuncture with existing inspection protocols. To complete this objective, multiple field deployments of a wired or custom developed wireless sensor system for performance assessment of stringer/multi-girder bridge superstructures was undertaken. Strain data was acquired for determining various load testing indices, such as the neutral axis (NA) of bending and transverse load distribution factors (DF). From this, a load rating using allowable stress design (ASD) and/or load resistance and factor rating (LRFR) was obtained for the superstructure. A quantitative assessment method for bridge inspection was developed utilizing the data collected from the experimental testing.;A challenge associated with developing new methods of damage detection and performance assessment is the insufficient quantity of quantitative performance data available in the bridge community. This work first gathers load testing data from multiple highway bridges in Northern New York with the main focus on an end-of-service life highway bridge. This particular 3 span simply supported bridge was first instrument with a wireless sensor system to measure the NA, DF and impact factor response under a known excitation. A load rating for each span is presented. The data indicated that for three identically designed spans the responses differed significantly showing the effects of the heavy deterioration. Upon completion of this test, a wired system was deployed on one of the spans where strain measurements were gathered at different levels of controlled damage implemented in various locations. The NA and DF were computed for each cases to see if the damage could be detected using these two indices. The data showed promising, but inconclusive, results to say with any level of certainty that using these parameters provides adequate detection of damage. As the implemented damage was not very severe, a drastic change in the data was not seen in many cases. Additional damage testing should be completed before any absolute conclusions can be drawn. Sensitivity analysis using the resolution of the sensor measurements was applied to both parameters for each load case. The data illustrated the importance of maintaining a high signal-to-noise ratio for more accurate results. In other words, for load testing of a particular element (e.g. girder), it is important to have the vehicle positioned over or near that element to minimize the noise effects.;The final part of this thesis incorporates the load testing data gathered from both tests mentioned previously to develop a performance index (PI) tool to be used for bridge inspection purposes. The PI was developed using the NA, DF and load rating of each girder and is specific to stringer/multi-girder bridges. The ratio of NA to DF (NA/DF) provided the input to the PI as they are related to the capacity and demand respectively of a girder and are load independent indices. An exponential model was fitted to the data and a 95 percent prediction band was developed around the model to provide the upper and lower bounds of the PI interval. The model was shown to work for this particular structure but when data from two different stringer/multi-girder bridges, also tested in this work, was implemented the data did not fall within the prediction bands. Therefore, while this model provides a good basis for quantitative assessment of stringer/multi-girder bridge superstructures, further developments that incorporate material and geometric properties of the superstructure need to be made to make this a universal fit for all bridges of this type. Nonetheless, this work improves the overall inspection process by providing bridge engineers with a quantitative performance assessment tool for more accurate and reliable condition ratings.