Predicting the Retroreflectivity Degradation of Thermoplastic Pavement Markings with Genetic Algorithm
Publication: Tran-SET 2022
ABSTRACT
In this study, genetic algorithm (GA) was employed to model the degradation of thermoplastic markings considering the key variables that are believed to have a significant effect on marking performance. A total of 4,608 skip retroreflectivity measurements were collected from three NTPEP test decks in Florida, and 12 predictive models were developed using GA. Apart from other key inputs, the first model was developed using the initially measured skip retroreflectivity, while remaining models used predicted values from the previous models. The developed GA models demonstrated an acceptable level of accuracy in terms of coefficient of determination (R2) and root mean square error (RMSE). The models predicted skip retroreflectivity of the thermoplastic markings for up to 1 year using only the initial measured skip retroreflectivity, which could assist transportation agencies to determine the expected service life of a marking product based on a specified retroreflectivity threshold and plan for future restriping activities.
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Tran-SET 2022
Pages: 146 - 155
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Published online: Dec 13, 2022
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