Developing a prediction model for rutting depth of warm mix asphalt mixture using neural network

Document Type : Original Article

Authors

• , Mahsa Rouhi fariman ¹
• Sayyed Ali Hosseini ²
• Mansour fakhri ³

¹ Faculty of Civil Engineering -Shahrood university of technology

² Faculty of Civil Engineering , Shahrood university of technology

³ Civil Engineering Faculty of K.N. Toosi University of Technology.

10.22067/jfcei.2022.74238.1104

Abstract

Researchers and engineers are constantly working to improve the performance of asphalt pavements. Pavements, as surfaces that are often loaded by heavy axles, must have sufficient resistance to fatigue, cracking and rutting. In this paper, using the data obtained from the laboratory results of the previous study that warm mix asphalt modified with glass fibers and 0, 20, 40 and 50% recycled asphalt pavement (RAP) were made to evaluate the resistance of the mixture against rutting, rutting depth of the mixtures was determined by multilayer perceptron neural network (MLP) and radial basis function neural network (RBF) and the results were compared with each other. The prediction model of post compaction and rutting depth showed good agreement with the experimental results. To evaluate the generalizability of the neural network using data that were not used during modeling, the multilayer perceptron neural network (MLP) performed better than the radial basis function neural network (RBF).

Keywords

• Recycle asphalt pavement
• Rutting
• Multilayer perceptron neural network
• Radial basis function neural network
• Post compaction

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doi: 10.5772/644.