Abstract
A four-layer neural network (NN) was constructed and applied to determine mapping associating factors in the design and testing of asphalt samples with their performance in repetitive rutting tests. A total of 1,586 samples (two samples per data point) were tested to determine rutting with the use of an asphalt pavement analyzer. Test results and mix volumetric properties were used to train the NN model. Preprocessing and principal component analysis were used, and the network was trained with the Levenberg–Marquardt algorithm. With randomly generated weighting factors to initialize the training algorithm, histograms were compiled, and outputs were estimated. Excellent agreement was observed between simulations and test data. The developed NN was used to estimate the optimum asphalt content of a Superpave® mix, and the results were satisfactory. The developed NN model will be a useful tool in the study of asphalt construction and wear.
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