Attempt at resilient modulus modeling using artificial neural networks

Erol Tutumluer, Roger W. Meier

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1-6
Number of pages6
JournalTransportation Research Record
Issue number1540
StatePublished - 1996

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


Dive into the research topics of 'Attempt at resilient modulus modeling using artificial neural networks'. Together they form a unique fingerprint.

Cite this