Determination of preconsolidation pressure with artificial neural network

Celik S., TAN Ö.

CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS, cilt.22, sa.4, ss.217-231, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 4
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1080/10286600500383923
  • Dergi Adı: CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.217-231
  • Anahtar Kelimeler: artificial neural network, preconsolidation, overconsolidated, clay, STRAIN-ENERGY, PREDICTION
  • Atatürk Üniversitesi Adresli: Evet

Özet

The correct determination of preconsolidation pressure is significantly important for settlement analysis in clay deposits. Many graphical methods have been developed by researchers for determining of preconsolidation pressure up to date. Some of these methods are Casagrande, Tavenas, Butterfield, Schmertmann and Janbu. Over the last few years, the use of artificial neural network (ANN) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. In this study, using professional software called Statistica, an ANN model was developed to determine preconsolidation pressures in clay soils. Results from the model and graphical methods (Casagrande, Tavernas, and Butterfield) were compared with actual (experimental) preconsolidation pressures and each other. In comparison with the statistical results of the graphical methods, the ANN model yielded larger determination coefficient (R-2 = 0.961), lower standard deviation ratio (0.198), lower mean absolute error (36.933) and lower root mean square error (48.169).