American Journal of Civil Engineering and Architecture
ISSN (Print): 2328-398X ISSN (Online): 2328-3998 Website: Editor-in-chief: Dr. Mohammad Arif Kamal
Open Access
Journal Browser
American Journal of Civil Engineering and Architecture. 2020, 8(2), 52-55
DOI: 10.12691/ajcea-8-2-4
Open AccessArticle

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

Noha K. Farh1, , Ahmed M. Awed1 and Sherif M. El-Badawy1

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

Pub. Date: May 06, 2020

Cite this paper:
Noha K. Farh, Ahmed M. Awed and 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


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.

resilient modulus; artificial neural networks subgrade proctor compaction moisture content

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  H. B. Seed, C. K. Chan, and C. E. Lee, “Resilience characteristics of subgrade soils and their relation to fatigue failures in asphalt pavements,” in International Conference on the Structural Design of Asphalt Pavements. SupplementUniversity of Michigan, Ann Arbor, 1962.
[2]  J. Uzan, “Characterization of granular material,” Transp. Res. Rec., vol. 1022, no. 1, pp. 52-59, 1985.
[3]  F. Lekarp, U. Isacsson, and A. Dawson, “State of the art. I: Resilient response of unbound aggregates,” J. Transp. Eng., vol. 126, no. 1, pp. 66-75, 2000.
[4]  D. Andrei, M. W. Witczak, and W. N. Houston, “Resilient modulus predictive model for unbound pavement materials,” in Contemporary Topics in Ground Modification, Problem Soils, and Geo-Support, 2009, pp. 401-408.
[5]  M. Ba, “Effect of Compaction Moisture Content on the Resilient Modulus of Unbound Aggregates from Senegal (West Africa),” Geomaterials, vol. 02, no. 01, pp. 19-23, 2012.
[6]  Y. Yao, J. Zheng, J. Zhang, J. Peng, and J. Li, “Model for Predicting Resilient Modulus of Unsaturated Subgrade Soils in South China,” KSCE J. Civ. Eng., vol. 22, no. 6, pp. 2089-2098, 2018.
[7]  A. M. Rahim and K. P. George, “Models to estimate subgrade resilient modulus for pavement design,” Int. J. Pavement Eng., vol. 6, no. 2, pp. 89-96, 2005.
[8]  C. L. Monismith, H. B. Seed, F. G. Mitry, and C. Chan, “Predictions of pavement deflections from laboratory tests,” in Second International Conference on the Structural Design of Asphalt PavementsUniversity of Michigan, Ann Arbor, 1967.
[9]  P. Kolisoja, Resilient deformation characteristics of granular materials. Tampere University of Technology Finland, Publications, 1997.
[10]  A. Cabrera, “Evaluation of the laboratory resilient modulus test using a New Mexico subgrade soil,” 2012.
[11]  A. S. El-Ashwah, A. M. Awed, S. M. El-Badawy, and A. R. Gabr, “A new approach for developing resilient modulus master surface to characterize granular pavement materials and subgrade soils,” Constr. Build. Mater., vol. 194, pp. 372-385, 2018.
[12]  R. Mousa, A. Gabr, M. G. Arab, A. Azam, and S. El-Badawy, “Resilient modulus for unbound granular materials and subgrade soils in Egypt,” in MATEC Web of Conferences, 2017, vol. 120, p. 6009.
[13]  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.
[14]  M. A. Shahin, M. B. Jaksa, and H. R. Maier, “Artificial neural network applications in geotechnical engineering,” Aust. Geomech., vol. 36, no. 1, pp. 49-62, 2001.
[15]  Y. M. Najjar, I. A. Basheer, H. E. Ali, and R. L. McReynolds, “Swelling potential of Kansas soils: Modeling and validation using artificial neural network reliability approach,” Transp. Res. Rec., vol. 1736, no. 1, pp. 141-147, 2000.
[16]  R. Ranasinghe, M. B. Jaksa, Y. L. Kuo, and F. P. Nejad, “Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results,” J. Rock Mech. Geotech. Eng., vol. 9, no. 2, pp. 340-349, 2017.
[17]  R. W. Meier, D. R. Alexander, and R. B. Freeman, “Using artificial neural networks as a forward approach to backcalculation,” Transp. Res. Rec., vol. 1570, no. 1, pp. 126-133, 1997.
[18]  S. Sharma and A. Das, “Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network,” Can. J. Civ. Eng., vol. 35, no. 1, pp. 57-66, 2008.
[19]  M. S. S. Far, B. S. Underwood, S. R. Ranjithan, Y. R. Kim, and N. Jackson, “Application of artificial neural networks for estimating dynamic modulus of asphalt concrete,” Transp. Res. Rec., vol. 2127, no. 1, pp. 173-186, 2009.
[20]  ASTM D698 - 12e2, “Standard Test Methods for Laboratory Compaction Characteristics of Soil Using Standard Effort (12 400 ft-lbf/ft3 (600 kN-m/m3)),” 2012.
[21]  ASTM:D1883, “Standard Test Method for CBR ( California Bearing Ratio ) of Laboratory-Compacted,” 2016.
[22]  ASTM D4318, “Standard Test Methods for Liquid Limit , Plastic Limit , and Plasticity Index of Soils,” 2017.
[23]  ASTM: D422 - 63, “ASTM D422: Standard Test Method for Particle-Size Analysis of Soils,” ASTM Stand. Guid., vol. i, no. Reapproved 2007, pp. 1-8, 2007.
[24]  ASTM:D2487, “Standard Practice for Classification of Soils for Engineering Purposes ( Unified Soil Classification System ),” 2017.
[25]  AASHTO T307, “Standard method of test for determining the resilient modulus of soils and aggregate materials,” Am. Assoc. State Highw. Transp. Off. Washingt., vol. 99, 2017.