A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks

Authors

  • L. Arabet Laboratory LMGHU, University of Skikda, Algeria
  • M. Hidjeb Engineering Department, University of Skikda, Algeria
  • F. Belaabed Civil and Hydraulic Engineering Department, University of Jijel, Algeria
Volume: 12 | Issue: 6 | Pages: 9795-9801 | December 2022 | https://doi.org/10.48084/etasr.5394

Abstract

For the prediction of the shear strength of reinforced soil many approaches are utilized which are complex and they depend on laboratory tests and several parameters. In this study, we aim to investigate and compare the ability of the Gray and Ohashi (GO) model and Artificial Neural Networks (ANNs) to predict the shear strength of reinforced soil. To achieve this objective, this work was divided into two parts. In the first part and in order to evaluate the impact of different fiber reinforcing parameters on the behavior of the soil, many direct shear experiments were carried out. The results revealed a significant improvement in shear strength values with fiber reinforcement. The increase in shear strength is a function of the fiber length, proportion, and direction. In the second part, we used the results of our experimental study to develop the ANN model. The obtained results agree reasonably well with the experiment ones, with very acceptable error (RMSE =1.714, MAE=5.981, R2= 0.960, and E = -1.601%). The comparative study showed that the ANN model was more accurate and statistically more stable than the GO model, and the ANN model took all the conditions of the reinforced soil into one equation. On the other hand, the GO model does not take reinforcement failure and uses several equations.

Keywords:

Gray & Ohashi model, natural fibers, shear strength, reinforced soil, artificial neural networks

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How to Cite

[1]
L. Arabet, M. Hidjeb, and F. Belaabed, “A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9795–9801, Dec. 2022.

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