Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

Isik F., ÖZDEN G.

ENVIRONMENTAL EARTH SCIENCES, vol.69, no.7, pp.2287-2297, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 69 Issue: 7
  • Publication Date: 2013
  • Doi Number: 10.1007/s12665-012-2057-5
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2287-2297
  • Keywords: Soil compaction, Neural networks, Computer modeling, Transition fine content, CONTACT DENSITY INDEXES, GRANULAR MIXES, PREDICTION, SETTLEMENT, BEHAVIOR, STRESS, SAND
  • Dokuz Eylül University Affiliated: Yes


This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (gamma(dmax)) and optimum water content (omega(opt)). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.