Novel Approach to Resilient Modulus Using Routine Subgrade Soil Properties
Publication: International Journal of Geomechanics
Volume 14, Issue 6
Abstract
Gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design applications. A database used for building the model was developed that contained grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in evaluation of the models developed. Two different correlations were developed using different combinations of the influencing parameters. The proposed constitutive models relate the resilient modulus of routine subgrade soils to moisture content , dry density , plasticity index (PI), percent passing a No. 200 sieve (), unconfined compressive strength , deviatoric stress , and bulk stress . The results are compared with those from artificial neutral network (ANN) models. Overall, GEP models show good performance and are proven to be better than ANN models. The GEP-based design equations can be readily used for pavement design purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses.
Get full access to this article
View all available purchase options and get full access to this article.
References
AASHTO. (2004). Standard specifications for transportation materials and methods of sampling and testing, Washington, DC.
Akin, S., and Karpuz, C. (2008). “Estimating drilling parameters for diamond bit drilling operations using artificial neural networks.” Int. J. Geomech., 68–73.
Alavi, A. H., and Gandomi, A. H. (2011). “A robust data mining approach for formulation of geotechnical engineering systems.” Eng. Comput., 28(3), 242–274.
Banzhaf, W., Nordin, P., Keller, R., and Francone, F. D. (1998). Genetic programming: An introduction. On the automatic evolution of computer programs and its application, Morgan Kaufmann, San Francisco.
Cevik, A., and Cabalar, A. F. (2008). “A genetic-programming-based formulation for the strength enhancement of fiber-reinforced-polymer-confined concrete cylinders.” J. Appl. Polym. Sci., 110(5), 3087–3095.
Dai, S. T., and Zollars, J. (2002). “Resilient modulus of Minnesota road research project subgrade soil.” Transportation Research Record 1786, Transportation Research Board, Washington, DC, 20–28.
Drumm, E. C., Boateng-Poku, Y., and Pierce, T. J. (1990). “Estimation of subgrade resilient modulus from standard tests.” J. Geotech. Engrg., 774–789.
Dunlap, W. S. (1963). “A report on a mathematical model describing the deformation characteristics of granular materials.” Tech. Rep. No.1, Project 2-8-62-27, Texas A&M Transportation Institute, Texas A&M Univ., College Station, TX.
Ebrahimi, A. (2006). Regression and neural network modeling of resilient modulus based on routine soil properties and stress states, Univ. of Oklahoma, Norman, OK.
Farrar, M. J., and Turner, J. P. (1991). “Resilient modulus of Wyoming subgrade soils.” Mountain Plains Consortium Rep. No. 91-1, Univ. of Wyoming, Laramie, WY.
Ferreira, C. (2001). “Gene expression programming: A new adaptive algorithm for solving problems.” Complex Syst., 13(2), 87–129.
Gandomi, A. H., Alavi, A. H., Mirzahosseini, M. R., and Nejad, F. M. (2011a). “Nonlinear genetic-based models for prediction of flow number of asphalt mixtures.” J. Mater. Civ. Eng., 248–263.
Gandomi, A. H., Tabatabaie, S. M., Moradian, M. H., Radfar, A., and Alavi, A. H. (2011b). “A new prediction model for load capacity of castellated steel beams.” J. Constr. Steel Res., 67(7), 1096–1105.
George, K. P. (2004). “Prediction of resilient modulus from soil index properties.” Rep. No. FHWA/MS-DOT-RD-04-172, Univ. of Mississippi, Jackson, MS.
Khazanovich, L., Celauro, B., Chabourn, B., and Zollars, J. (2006). “Evaluation of subgrade resilient modulus predictive model for use in mechanistic-empirical pavement design guide.” Transportation Research Record 1947, Transportation Research Board, Washington, DC, 155–166.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection, MIT Press, Cambridge, MA.
Malla, R. B., and Joshi, S. (2007). “Resilient modulus prediction models based on analysis of LTPP data for subgrade soils and experimental verification.” J. Transp. Eng., 491–504.
Malla, R. B., and Joshi, S. (2008). “Subgrade resilient modulus prediction models for coarse and fine-grained soils based on long-term pavement performance data.” Int. J. Pavement Eng, 9(6), 431–444.
Mayoraz, F., and Vulliet, L. (2002). “Neural networks for slope movement prediction.” Int. J. Geomech., 153–173.
Miranda, T., Correia, A. G., Santos, M., Sousa, L. R., and Cortez, P. (2011). “New models for strength and deformability parameter calculation in rock masses using data-mining techniques.” Int. J. Geomech., 44–58.
Mohammad, L. N., Huang, B., Puppala, A. J., and Allen, A. (1999). “Regression model for resilient modulus of subgrade soils.” Transportation Research Record 1442, Transportation Research Board, Washington, DC, 47–54.
Mollahasani, A., Alavi, A. H., and Gandomi, A. H. (2011). “Empirical modeling of plate load test moduli of soil via gene expression programming.” Comput. Geotech., 38(2), 281–286.
Moossazadeh, J. M., and Witczak, M. W. (1981). “Prediction of subgrade moduli for soil that exhibits nonlinear behavior.” Transportation Research Record 810, Transportation Research Board, Washington, DC, 9–17.
National Cooperative Highway Research Program (NCHRP). (2004). “Guide for mechanistic-empirical design of new and rehabilitated pavement structures.” Final Rep. No. NCHRP 1-37A, National Cooperative Highway Research Program, National Research Council, Transportation Research Board, Washington, DC.
Park, H. I., Kweon, G. C., and Lee, S. R. (2009). “Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network.” Road Mater. Pavement Des., 10(3), 647–665.
Samui, P., and Sitharam, T. G. (2010). “Site characterization model using artificial neural network and kriging.” Int. J. Geomech., 171–180.
Santha, B. L. (1994). “Resilient modulus of subgrade soils: Comparison of two constitutive equations.” Transportation Research Record 1462, Transportation Research Board, Washington, DC, 79–90.
Schuettpelz, C. C., Fratta, D., and Edil, T. B. (2010). “Mechanistic corrections for determining the resilient modulus of base course materials based on elastic wave measurements.” J. Geotech. Geoenviron. Eng., 1086–1094.
Seed, H. B., Mitry, F. G., Monismith, C. L., and Chan, C. K. (1967). “Prediction of flexible pavement deflections from laboratory repeated-load tests.” NCHRP Rep. No. 35, National Cooperative Highway Research Program (NCHRP), Washington, DC.
Smith, G. N. (1986). Probability and statistics in civil engineering, Collins, London.
Thompson, M. R., and Robnett, Q. L. (1976). “Resilient properties of subgrade soils.” Transp. Engrg. J., 105(1), 71–89.
Uzan, J. (1985). “Characterization of granular material.” Transportation Research Record 1022, Transportation Research Board, Washington, DC.
Von Quintus, H., and Killingsworth, B. (1998). “Analysis relating to pavement material characterizations and their effects on pavement performance.” Rep. No. FHWA-RD-97-085, Federal Highway Administration, U.S. DOT, Washington, DC.
Yau, A., and Von Quintus, H. L. (2002). “Study of LTPP laboratory resilient modulus test data and response characteristics.” Final Rep. No. FHWA-RD-02-051, Federal Highway Administration, U.S. DOT, Washington, DC.
Yeh, S. T., and Su, C. K. (1989). “Resilient properties of Colorado soils.” Rep. No. CDOH-DH-SM-89-9, Colorado Dept. of Highways, Denver.
Zaman, M., Solanki, P., Ebrahimi, A., and White, L. (2010). “Neural network modeling of resilient modulus using routine subgrade soil properties.” Int. J. Geomech., 1–12.
Zhang, G., Xiang, X., and Tang, H. (2011). “Time series prediction of chimney foundation settlement by neural networks.” Int. J. Geomech., 154–158.
Information & Authors
Information
Published In
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Mar 18, 2013
Accepted: Oct 21, 2013
Published online: Oct 23, 2013
Discussion open until: Sep 1, 2014
Published in print: Dec 1, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.