The use of neural networks for CPT-based liquefaction screening

ERZİN Y., Ecemis N.

Bulletin of Engineering Geology and the Environment, cilt.74, sa.1, ss.103-116, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 74 Sayı: 1
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s10064-014-0606-8
  • Dergi Adı: Bulletin of Engineering Geology and the Environment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.103-116
  • Anahtar Kelimeler: Artificial neural networks, Cone penetration resistance, Liquefaction resistance, Ottowa sand
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

This study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.