


Volume 16 No 6 (2018)
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Performance Evaluation of Asphalt Pavement Based on BP Neural Network
Lili He, Han Zhu, Zhanxu Gao
Abstract
Asphalt pavement is widely used in road pavement because of its high temperature stability, low temperature crack
resistance, water stability, fatigue resistance and many other advantages. However, with the increase of the service
time, and the long-term effects of the surrounding environment and traffic loads, it is easy to cause cracks,
subsidence, and water damage on the asphalt pavement, which brings enormous financial pressure to maintenance.
For this reason, this study proposes an evaluation and prediction model of pavement performance based on BP
artificial neural network through the analysis of the characteristics of pavement performance evaluation and use of
the multi-factor fuzzy computing capability of artificial neural network algorithm, and adopts this model to predict
the rut depth and flatness of asphalt pavements. The results show that the prediction data is in good agreement
with the actual measured data, with high accuracy and small errors, which proves that the model has extremely
high operability. This provides a theoretical basis for the prediction of asphalt pavement performance, which is of
great practical significance to reduce the cost of asphalt pavement maintenance and improve the performance of
asphalt pavement
Keywords
Asphalt Pavement, Performance Evaluation, BP Neural Network, Prediction Model
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