National Concrete Pavement Technology Center

Project description

Two Nebraska bridges with asphalt overlay were selected for nondestructive testing and evaluation (NDT/NDE). Three NDT techniques were conducted on these two bridges, including Ground Penetrating Radar (GPR), Half-Cell Potential (HCP) and Unmanned Aerial Vehicle (UAV) imaging. NDT data were collected during three construction stages of the bridges: (1) before repair on existing asphalt overlay, (2) on bare concrete after asphalt removal, (3) and after repairing delaminated concrete. A machine learning technique, autoencoder, was used to build quantitative relationships between different NDT datasets. On bare concrete, the GPR amplitude and HCP voltage show a strong linear relationship. Then a threshold for GPR amplitude (-6.4 dB) can be determined based on the well-established HCP criteria. The GPR amplitudes on asphalt overlay also show a clear correlation with GPR amplitudes on bare concrete. Direct comparison of two GPR amplitude maps indicates GPR data collected on asphalt overlay could detect most severely deteriorated areas but may miss some mild deterioration. A big data image pipeline was created for mapping cracks and repair patches with images collected from an UAV. Comparing surface defects on asphalt overlay with HCP and GPR data suggests that UAV images may be used as an initial screening tool for extending NDT inspection of bridge decks. Further studies are needed to evaluate the performance of UAV imaging based visual inspection through quantitative analysis of surface defects and severity of deterioration.