The present research project investigates monitoring concrete precast panels for bridge decks that are reinforced with Glass Fiber Reinforced Polymer (GFRP) bars. Due to the lack of long term research on concrete members reinforced with GFRP bars, long term health monitoring is important to record the performance and limit states of the GFRP decks and bridge as a whole. In this research, data is collected on concrete strains, bridge deflections, vertical girder accelerations, as well as initial truck load testing and lifting strains.

The present research project investigates lightweight and normal weight concrete precast panels for highway bridge decks. The deck panels are reinforced with Glass Fiber Reinforced Polymer (GFRP) bars. Due to the lack of research on lightweight concrete members reinforced with GFRP bars, the AASHTO LRFD Bridge Design Guide Specifications for GFRP Reinforced Concrete Decks do not permit the use of lightweight concrete when GFRP bars are used as flexural reinforcement. In this research, the experimental performance of lightweight concrete versus normal weight concrete precast GFRP reinforced deck panels is investigated in terms of flexural capacity, panel deflections, and shear capacity.

A total of 6 full-scale glass fiber-reinforced polymer (GFRP) composite bridge deck specimens were tested to study the significance of using acoustic emission (AE) for monitoring and analysis of the structural integrity of the specimens during a predetermined loading profile. The first two specimens varied in width and were loaded to failure and last four specimens were the original specimens that were repaired after failure occurred using an FRP wrap. The objective, through the use of AE monitoring and analysis, is to identify failure prediction criteria and/or a methodology that would provide a determination of the structural integrity of the in-service FRP bridge deck during field inspection. While no codes and standards exist for these types of specimens, current standards developed for FRP tanks and vessels were used as a base reference to determine if current standards could be adopted or if new or additional criteria needed to be established. Real-time monitoring was conducted for each specimen during a standard 3-point bending test. Monitoring typically covered loading up to 80% of the calculated ultimate strength. During monitoring, a selected set of features associated with each AE hit and the associated waveform were recorded in a database for post analysis. The collected data was later analyzed using comparison and intensity analysis, linear location and waveform analysis, accompanied with pattern recognition, to identify series of hits with a particular event. Each event was investigated to determine if the type of damage, such as fiber breakage, matrix cracking, and delamination, could characterize the event. These types of events were the contributing factors to the investigated criteria and the structural performance of the specimens. In post analysis, comparison analysis was performed to observe the Kaiser Effect and the calculation of a Felicity Ratio when the Kaiser Effect broke down. For the original specimens, the Felicity Ratio fell within expected values observed from previous work, while the repaired specimens, using an external FRP wrap, were generally higher than the typically accepted value of 0.85. The second type of post analysis, linear location, was performed to pinpoint the location along the axis of the specimen in which the majority of the events occurred. In the case of the original specimens, visual inspection was difficult as the majority of the damage of the specimen occurred at the inner core. While there is some associated stress redistribution that leads to delamination of the outer flutes from the top and bottom face panels, this was the only visually observable change for the original specimens. Thus, linear location becomes an important tool for the location and isolation of major damage before reaching catastrophic failure. The failure mode of the repaired specimens was restricted due to the external wrap, and provided a visual cue of damage. The third type of analysis, waveform analysis using pattern recognition, appears promising in identifying each type of damage characteristic and training a neural network to classify incoming waveforms. This damaged-based characterization could be useful for in-field service inspection. However, further investigation is needed for verification before using this form of classification.