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- W751258390 abstract "One of the most important data requirements for pavement management systems is pavement condition, including such aspects as surface distress, profile, structural response, and skid resistance. Pavement distress includes characteristics of pavement fracture, distortion, and disintegration. The pavement crack is one of the most important distress types. However, there is no widely accepted measure for cracking in pavements. Presently, the most widely used distress detection method is by manual inspection. This approach serves well and is simple to implement. However it possesses many shortcomings. The most obvious ones are the inefficiency of time and labor. In addition, the safety of raters is endangered due to high-speed traffic on highways. Inconsistent rating also complicates the reliability of field surveys. During the last few years, a number of automated distress measuring devices have been developed using advanced image processing techniques. New image processing algorithms are still being introduced, and the automated devices are continuously being improved with respect to their performance. These automated image processing techniques have the following merits: (1) Both time and labor are saved; (2) Raters are not exposed to traffic because the detection is done in the lab; and (3) Consistent ratings are obtained as a result of using the same set of algorithms. This paper discusses the development of a pattern recognition model for pavement distress by utilizing artificial neural network (ANN) techniques. The discussion is focused on the development of distress image-processing algorithms, which determines the type of distress pattern on a pavement. The types of distresses used in the model are alligator, longitudinal, transversal, block and diagonal cracks and the ANN typology used is the back-propagation method. (a) For the covering entry of this conference, please see ITRD abstract no. E204151." @default.
- W751258390 created "2016-06-24" @default.
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- W751258390 date "2000-01-01" @default.
- W751258390 modified "2023-09-28" @default.
- W751258390 title "PATTERN RECOGNITION FOR PAVEMENT DISTRESS BY USING ARTIFICIAL NEURAL NETWORK TECHNIQUES" @default.
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