Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts

Tekin E., AKBAŞ S. O.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, vol.70, no.1, pp.153-161, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 70 Issue: 1
  • Publication Date: 2011
  • Doi Number: 10.1007/s10064-010-0295-x
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.153-161
  • Keywords: Groutability, Artificial neural networks, Granular soil, Microfine cement, COHESIONLESS SOILS, PREDICTION, MODEL, SETTLEMENT, PRESSURE, CAPACITY
  • Gazi University Affiliated: Yes


A reliable estimation of the groutability of the target geomaterial is an essential part of any grouting project. An artificial neural network (ANN) model has been developed for the estimation of groutability of granular soils by cement-based grouts, using a database of 87 laboratory results. The proposed model used the water:cement ratio of the grout, relative density of the soil, grouting pressure, and diameter of the sieves through which 15% of the soil particles and 85% of the grout pass. A very good correlation was obtained between the ANN predictions and the laboratory experiments. Comparison of these results with those obtained using traditional methods for groutability prediction confirmed the viability of using ANN to estimate groutability.