Artificial neural networks approach to predicting rut depth of asphalt concrete by using of visco-elastic parameters. (15th January 2018) - Kamboozia, N.; Ziari, H.; Behbahani, H.
Highlights: Proposed ANN model for rut depth has shown good agreement with experimental data. Proposed model is able to predict rut depth with an acceptable degree of accuracy. Proposed ANN model is valid for the ranges of the experimental database. Based on the results, the proposed model shows less sensitivity to loading time. Proposed model can estimate rut depth of asphalt based on effective parameters. Abstract: Performing comprehensive research on the functional behavior of asphalt pavements under the influence of various environmental and structural parameters can better assist the engineers in the design and maintenance of asphalt pavements. Using a solution that can reduce the cost and time of assessment is very important. Using artificial neural networks in many facilitates operations on data engineering sciences. It is necessary to ensure that a comprehensive study is performed through considering all or most of the parameters affecting the behavior. The aim of this study is to provide an experimental model to estimate the rut depth of asphalt concrete by using viscoelastic parameters and artificial neural networks. Accordingly the asphalt concrete specimens containing 3, 5 and 7 percent void with two types of limestone and siliceous aggregates and PG64-22 and PG58-28 bitumens were made and exposed to dynamic creep tests under 50–60 °C and the stress range of 100–300 kPa. Then the viscoelastic parameters of asphalt specimens were extracted from the creep diagrams and eventually the asphalt concrete's rut depth prediction model was trained and provided by artificial neural network. Comparing the output results with the experimental test results show that by using this model it is possible to estimate the creep behavior and rut depth of asphalt concrete pavements based on the effective parameters without the need for costly and time-consuming tests.