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Geomechanics and Engineering
  Volume 12, Number 3, March 2017 , pages 441-464
DOI: https://doi.org/10.12989/gae.2017.12.3.441
 


On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence
Hamza Güllü and Halil Íbrahim Fedakar

 
Abstract
    The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS (p
 
Key Words
    freeze-thaw cycle; unconfined compressive strength; silty soil; artificial intelligence; sensitivity analysis; bottom ash; jute fiber; steel fiber
 
Address
Department of Civil Engineering, University of Gaziantep, 27310, Gaziantep, Turkey.
 

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