The pitfalls inherent in the indiscriminate application of artificial neural networks to numerical modeling problems are illustrated. An example is used of an apparently successful (but ultimately unsuccessful) attempt at training a neural network constitutive model for computing the resilient modulus of gravels as a function of stress state and various material properties. Issues such as the quantity and quality of data needed to successfully train a neural network are explored, and the importance of an independent test set to verify network performance is examined.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Attempt at Resilient Modulus Modeling Using Artificial Neural Networks


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    1996-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Attempt at Resilient Modulus Modeling Using Artificial Neural Networks

    Tutumluer, Erol / Meier, Roger | Transportation Research Record | 1996


    Prediction of subgrade resilient modulus using artificial neural network

    Kim, Sung-Hee / Yang, Jidong / Jeong, Jin-Hoon | Springer Verlag | 2014


    Prediction of subgrade resilient modulus using artificial neural network

    Kim, Sung-Hee / Yang, Jidong / Jeong, Jin-Hoon | Online Contents | 2014


    Backcalculation of Dynamic Modulus from Resilient Modulus of Asphalt Concrete with an Artificial Neural Network

    Andrew, Lacroix / Kim, Y. Richard / Ranjithan, S. Ranji | Transportation Research Record | 2008