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

Gullu H., Fedakar H. İ.

GEOMECHANICS AND ENGINEERING, vol.12, no.3, pp.441-464, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 3
  • Publication Date: 2017
  • Doi Number: 10.12989/gae.2017.12.3.441
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.441-464
  • Keywords: freeze-thaw cycle, unconfined compressive strength, silty soil, artificial intelligence, sensitivity analysis, bottom ash, jute fiber, steel fiber, REGRESSION NEURAL-NETWORK, FUZZY INFERENCE SYSTEM, INDUSTRIAL-WASTES, TENSILE-STRENGTH, FLOW PREDICTION, GRANULAR SOILS, BEHAVIOR, SANDS, PARAMETERS, SETTLEMENT
  • Abdullah Gül University Affiliated: Yes


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 (R-2) 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 <= 0.05). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and R-2 = 0.988). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.