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Neural Computing and Applications
Published in: Neural Computing and Applications 4/2019

07-07-2017 | Original Article

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

Authors: Erhan Tekin, Sami Oguzhan Akbas

Published in: Neural Computing and Applications | Issue 4/2019

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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.
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Appendix
Available only for authorised users
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Metadata
Title
Predicting groutability of granular soils using adaptive neuro-fuzzy inference system
Authors
Erhan Tekin
Sami Oguzhan Akbas
Publication date
07-07-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 4/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3140-3

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