Abstract
Ground penetrating radar (GPR) has been developed and used successfully for bridge deck and roadway condition assessment. In the past, GPR interpretation has been done manually by trained engineers and technicians with the aid of standard signal processing techniques. This method of collection produced vast quantities of data, and the interpretation required a great amount of time. Recently, parallel processing in the form of artificial neural networks (ANNs) has been applied to the interpretation of GPR condition assessment data from highways. This paper introduces general strategy for using ANNs for the interpretation of GPR data. Results of applying this strategy to bridge deck condition assessment data are also given.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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