Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus

    Authors

    M. D. Nazzal;O. Tatari

    Comments

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    Abbreviated Journal Title

    Int. J. Pavement Eng.

    Keywords

    artificial neural network; genetic algorithms; resilient modulus; subgrade soils; MEPDG; HOT-MIX ASPHALT; DYNAMIC MODULUS; MODELS; Construction & Building Technology; Engineering, Civil; Materials; Science, Characterization & Testing

    Abstract

    This paper investigates the use of artificial neural networks (ANNs) and genetic algorithms to improve the accuracy of the prediction of subgrade resilient modulus (M-r) based on soil index properties. Furthermore, it also examines the effect of the accuracy of the M-r estimation on the mechanistic empirical pavement design guide (MEPDG) performance prediction. The results of this paper showed that the ANN models had much better prediction of the M-r coefficients of subgrade soils than that of the regression models. In addition, the use of the genetic algorithms in the selection of the input variables of the ANN models enhanced the accuracy of the prediction of those models. The results of the MEPDG analyses indicated that the prediction model used to estimate the subgrade M-r input value can have a significant effect on the predicted performance of pavements. Furthermore, those results showed that the use of ANN models yielded much more accurate pavement performance prediction than using regression models; in particular when genetic algorithms were used in developing those models.

    Journal Title

    International Journal of Pavement Engineering

    Volume

    14

    Issue/Number

    4

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    364

    Last Page

    373

    WOS Identifier

    WOS:000317755100005

    ISSN

    1029-8436

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