Artificial intelligence applied to bridge deck radar interpretation

Abstract

<p>The use of ground probing radar for the nondestructive evaluation of structures has been in use for more than a decade. Through the examination of the subsurface reflection waveforms, determinations may be made regarding the profile geometry as well as detecting the presence of structural faults undetectable from surface inspections. Although automated systems have been previously created, the arduous task of interpretation of the large volumes of data produced by these radar surveys are still best handled by human experts. The research presented in this dissertation details the development of novel techniques which allow an Artificial Intelligence based system to demonstrate comprehensive structural and fault analysis capabilities. Development of an autonomously defined and system trained neural network based radar waveform filter allows reflection event feature extraction. The three-dimensional concept of the bridge structures and the system's ability to accurately model the diverse cross-sections are combined with the neural preprocessed data to allow data abstraction. The positional relationship present in bridge radar surveys are exploited to develop a unique spatiotemporal foundation for analysis. The comprehensive expert system was tested with real bridge radar data and the system results correlate well with human expert analysis.</p>