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Artificial Neural Network Models for the Estimation of the Optimum Asphalt Content of Asphalt Mixtures

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Abstract

The aggregate gradation has a significant influence on the required optimum asphalt content (OAC) on the asphalt mix as the OAC is mainly responsible for coating the aggregate surface area and filling the voids in the aggregate particles. In general, laboratory tests are used for the estimation of the OAC. In Egypt, Marshall test is the most common test used for the estimation of the OAC. However, the procedures of this approach consume significant time and its results are subjected to deviations. Thus, this paper employs the multilayer perceptron feedforward neural network technique for the prediction of the OAC from the aggregate gradation for 140 asphalt mix samples. To optimize the settings and hyperparameters of the ANN, 240 different artificial neural networks (ANNs) with different number of hidden layers, different number of neurons per hidden layers, and different activation functions are tested to find the optimum ANN with the best predictions and lowest errors. Results show that the optimum ANN uses the logistic activation function and consists of four hidden layers with two neurons per layer. This optimum ANN can predict the OAC with R2 value of 0.982 when used on the testing set. Additionally, the optimum ANN was validated on three datasets from the literature to test the ability of the ANN to generalize and results show that the optimum ANN can predict the OAC with high accuracy for asphalt mixes prepared in different countries and with different properties such as the aggregate gradation, and bitumen type.

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Abbreviations

OAC:

Optimum asphalt content

ANN:

Artificial neural network

%P(I):

Percentage passing from sieve i

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  1. Civil Engineering Department, University of Toronto, 35 St George St, Toronto, ON, M5S 1A4, Canada

    Kareem Othman

  2. Public Works Department, Faculty of Engineering, Cairo University, Giza, Egypt

    Kareem Othman

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Othman, K. Artificial Neural Network Models for the Estimation of the Optimum Asphalt Content of Asphalt Mixtures. Int. J. Pavement Res. Technol. 16, 1059–1071 (2023). https://doi.org/10.1007/s42947-022-00179-6

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