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Neural Computing and Applications

, Volume 31, Issue 4, pp 1091–1101 | Cite as

Predicting groutability of granular soils using adaptive neuro-fuzzy inference system

  • Erhan TekinEmail author
  • Sami Oguzhan Akbas
Original Article

Abstract

In this paper, the applicability of adaptive neuro-fuzzy inference system (ANFIS) for the prediction of groutability of granular soils with cement-based grouts is investigated. A database of 117 grouting case records with relevant geotechnical information was used to develop the ANFIS model. The proposed model uses the water–cement ratio of the grout, the relative density and fines content of the soil, the grouting pressure, and the ratio between the particle size of the soil corresponding to 15% finer and that of grout corresponding to 85% finer as input parameters. The accuracy of the proposed ANFIS model in terms of the corresponding coefficient of correlation (R) and root mean square error (RMSE) values is found to be quite satisfactory. Furthermore, a comparative analysis with existing groutability prediction methods indicates that the ANFIS model demonstrates superior performance.

Keywords

Groutability Adaptive neuro-fuzzy inference system (ANFIS) Granular soil Microfine cement 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

521_2017_3140_MOESM1_ESM.exe (882 kb)
Supplementary material 1 (EXE 883 kb)

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringGazi UniversityAnkaraTurkey

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