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M. G. Arab, R. A. Mousa, A. R. Gabr, A. M. Azam, S. M. El-Badawy, and A. F. Hassan, “Resilient Behavior of Sodium Alginate-Treated Cohesive Soils for Pavement Applications,” J. Mater. Civ. Eng., vol. 31, no. 1, p. 4018361, 2019.

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Article

Artificial Neural Network Model for Predicating Resilient Modulus of Silty Subgrade Soil

1Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt


American Journal of Civil Engineering and Architecture. 2020, Vol. 8 No. 2, 52-55
DOI: 10.12691/ajcea-8-2-4
Copyright © 2020 Science and Education Publishing

Cite this paper:
Noha K. Farh, Ahmed M. Awed, Sherif M. El-Badawy. Artificial Neural Network Model for Predicating Resilient Modulus of Silty Subgrade Soil. American Journal of Civil Engineering and Architecture. 2020; 8(2):52-55. doi: 10.12691/ajcea-8-2-4.

Correspondence to: Noha  K. Farh, Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. Email: engnohafarh@gmail.com

Abstract

Recently machine learning is gaining acceptance in different civil engineering applications. In this study, an Artificial Neural Network (ANN) model is proposed to predict resilient modulus (MR) of a silty subgrade soil for pavement designs. A silty subgrade soil was compacted at the maximum dry density (γdopt) and optimum moisture content (OMC) according to the standard Proctor compaction. The resilient modulus test was then conducted on at least replicate samples of three groups of samples. The first group of samples were tested directly after compaction, the second group and third groups, after compaction at the standard Proctor effort were left in open air to dry over time or exposed to wetting to gain moisture. The testing results were then used to develop the ANN model. This model predicts MR of the soil based on water content (Wc), ratio of dry density over the maximum dry density at the optimum moisture content (γddopt) and octahedral shear stress (τoct). After the ANN model architecture is set, the strengths and weaknesses of the developed model are examined by comparing the predicted versus measured MR values with respect to goodness-of-fit statistics. In addition, a sensitivity analysis of the model input parameters is performed.

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