Abstract
A combined laboratory and modeling study was undertaken to develop a database for common subgrade soils in Oklahoma and to develop relationships or models that could be used to estimate resilient modulus (MR) from commonly used subgrade soil properties in Oklahoma. Sixty-three soil samples from 14 different sites throughout Oklahoma are collected and tested for the development of the database and models. Additionally, thirty-four soil samples from 3 different sites, located in Rogers and Woodward counties, are collected and tested to evaluate the developed models. The routine material parameters selected in the development of the models include moisture content (w), dry density (γ d ), plasticity index (PI), percent passing No. 200 sieve (P200), and unconfined compressive strength (Uc). Bulk stress (θ) and deviatoric stress (σ d ) are used to identify the state of stress. A total of four, two regression models, namely, Polynomial and Factorial, and two feedforward-type artificial neural network (ANN) models, namely, Radial Basis Function Network (RBFN) and Multi-Layer Perceptrons Network (MLPN) are developed. A commercial software, STATISTICA 7.1, is used to develop these models. The strengths and weaknesses of the developed models are examined by comparing the predicted MR values with the experimental values with respect to the R2 values. An evaluation of the four models indicate that for the combined development and evaluation datasets, the MLPN model is a good model for evaluating MR from the selected routinely determined properties. In order to illustrate the application of the developed model, the AASHTO flexible pavement design methodology is used to design asphalt concrete pavement sections.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
AASHTO, Standard specifications for transportation materials and methods of sampling and testing. Transportation Research Board, National Research Council, Washington DC (1986)
American Association of State Highway and Transportation Officials (AASHTO), Guide for mechanistic-empirical design of new and rehabilitated pavement structures. Final report prepared for National Cooperative Highway Research Program. Transportation Research Board, National Research Council, Washington DC (2004)
Bishop, C.: Neural networks for pattern recognition. University Press, Oxford (1995)
Bors, A.G.: Introduction of the Radial Basis Function (RBF) Networks. Online Symp. for Electronics Engineers, DSP Algorithms: Multimedia 1(1), 1–7 (2001)
Carmichael III, R.F., Stuart, E.: Predicting resilient modulus: A study to determine the mechanical properties of subgrade soils. Transportation Research Record 1043, 20–28 (1978)
Dai, S., Zollars, J.: Resilient modulus of Minnesota road research project subgrade soil. Transportation Research Record 1786, 20–28 (2002)
Drumm, E.C., Boateng-Poku, Y., Pierce, T.J.: Estimation of subgrade resilient modulus from standard tests. Journal of Geotechnical Engineering 116(5), 774–789 (1990)
Dunlap, W.S.: A Report on a Mathematical Model Describing the Deformation Characteristics of Granular Materials. Technical Report 1, Project 2-8-62-27, TTI, Texas A&M University, Texas (1963)
Ebrahimi, A.: Regression and neural network modeling of resilient modulus based on routine soil properties and stress states. PhD dissertation, University of Oklahoma, Oklahoma (2006)
Far, M.S.S., Underwood, B.S., Ranjithan, S.R., Kim, Y.R., Jackson, N.: The application of artificial neural networks for estimating the dynamic modulus of asphalt concrete. In: Transportation Research Board 2009 Annual Meeting. CD-ROM Publication, Washington DC (2009)
Farrar, M.J., Turner, J.P.: Resilient modulus of wyoming subgrade soils mountain planins. Consortium Report No. 91-1, The University of Wyoming, Luramie, Wyoming (1991)
Fausett, L.V.: Fundamentals neural networks: Architecture, Algorithms and Applications. Prentice-Hall, Inc., Englewood Cliffs (1994)
Fernandez-Juricic, E.A., Sallent, R.S., Rodriguez-Prieto, I.: Testing the risk-disturbance hypothesis in a fragmented landscape: non-linear responses of house sparrows to humans. The Condor 105, 316–326 (2003)
FHWA (2002) Study of LTPP laboratory resilient modulus test data and response characteristics. Final report, Publication No. FHWA-RD-02-051, Office of Engineering Research and Development, McLean, Virginia (October 2002)
George, K.P.: Resilient Testing of soils using gyratory testing machine. Transportation Research Record 1369, 63–72 (1992)
Gomes, A.C., Gillett, S.: Flexible pavement: Resilient behavior of soils. Balkema, Rotterdam (1996) ISBN 90 54 10 5232
Haykin, W.L.: Neural networks: A comprehensive foundation. Macmillan College Publishing, New York (1994)
Hill, T., Lewicki, P.: STATISTICS methods and applications. StatSoft, Tulsa, Oklahoma (2006)
Hopkins, T.C., Beckham, T.L., Sun, L., Pfalzer, B.: Kentucky geotechnical database. Publication KTC-03-06/SPR-177-98-1F, University of Kentucky Transportation Center, College of Engineering, Lexington, Kentucky (2004)
Huang, Y.H.: Pavement analysis and design, 2nd edn. Prentice Hall, Upper Saddle River (2004)
Khazanovich, L., Celauro, B., Chabourn, B., Zollars, J.: Evaluation of subgrade resilient modulus predictive model for use in mechanistic-empirical pavement design guide. Transportation Research Record 1947, 155–166 (2006)
Kim, D., Kweon, G., Lee, K.: Alternative method of determining resilient modulus of compacted subgrade soils using free-free resonant column test. Transportation Research Record 1577, 62–69 (1997)
Kim, D., Stokoe II, K.H.: Characterization of resilient modulus of compacted subgrade soils using resonant column and torsional shear tests. Transportation Research Record 1369, 83–91 (1992)
Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer, Berlin (1989)
Lee, W., Bohra, N.C., Altschaeffl, A.G., White, T.D.: Resilient modulus of cohesive soils. ASCE Journal of Geotechnical and Geoenvironmental Engineering 123(2), 131–136 (1997)
Li, D., Selig, E.T.: Resilient modulus for fine-grained subgrade soils. ASCE Journal of Geotechnical Engineering 120(6), 939–957 (1994)
Malla, R.B., Joshi, S.: Subgrade resilient modulus prediction models for coarse and fine-grained soils based on long-term pavement performance data. International Journal of Pavement Engineering 9(6), 431–444 (2008)
May, R.W., Witczak, M.W.: Effective granular modulus to model pavement response. Transportation Research Record 810, 1–9 (1981)
Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of artificial neural networks. MIT Press, Cambridge (1996)
Meier, R.W., Alexander, D., Freeman, R.B.: Using artificial neural networks as a forward approach to backcalculation. Transportation Research Record 1570, 126–133 (1996)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to linear regression analyses. Wiley Series in Probability and Statistics. John Wiley and Sons, Inc., New Jersey (2006)
Moossazadeh, J.M., Witczak, M.W.: Prediction of subgrade moduli for soil that exhibits nonlinear behavior. Transportation Research Record 810, 9–17 (1981)
Myers, R.H., Montgomery, D.C., Vining, G.G.: Generalized linear models: With applications in engineering and the sciences. John Wiley and Sons, Inc., New Jersey (2001)
Najjar, Y.M., Basheer, I.A., Ali, H.E., McReynolds, R.L.: Swelling potential of kansas soils: modeling and validation using artificial neural network reliability approach Transportation. Research Record 1736, 141–147 (2000)
Narayan, S.: Using genetic algorithms to adapt neuron functional forms. In: Proc. Artificial Intelligence and Soft Computing, Banff, Canada (July 2002)
NCHRP, Procedure for resilient modulus of unstabilized aggregate base and subgrade materials. Project 1-28A, Transportation Research Board, Washington D.C (2003)
ODOT, Standards and specifications. Oklahoma Department of Transportation (2000)
Patterson, D.: Artificial neural networks. Prentice Hall, Singapore (1996)
Paute, J.L., Hornych, P.: Flexible pavement: Influence of water content on the cyclic behavior of a silty sand. Balkema, Rotterdam (1996)
Raad, L., Minassian, G.H., Gartin, S.: Characterization of saturated granular bases under repeated loads. Transportation Research Record 1369, 73–82 (1992)
Rada, C., Witczak, W.M.: Comprehensive evaluation of laboratory resilient moduli results for granular material. Transportation Research Record 810, 23–33 (1981)
Rahim, A.M., George, J.P.: Subgrade soil index properties to estimate resilient modulus. In: Transportation Research Board 2004 Annual Meeting. CD-ROM Publication, Transportation Research Board, Washington DC (2004)
Rahim, A.M., George, J.P.: Subgrade soil index properties to estimate resilient modulus. In: Transportation Research Board, Annual Meeting, CD-ROM Publication, Transportation Research Board, Washington DC (2004)
Rankine, R.M., Sivakugan, N.: Prediction of paste backfill performance using artificial neural networks. In: Proc. the 16th ISSMGE (2), Osaka, pp. 1107–1110 (2005)
Ripley, B.D.: Pattern recognition and neural networks. Cambridge University Press, Cambridge (1996)
Rumelhart, D.E., McClelland, J.: Parallel distributed processing, vol. 1. MIT Press, Cambridge (1986)
Seed, H.B., Mitry, F.G., Monosmith, C.L., Chan, C.K.: Prediction of pavement deflection from laboratory repeated load tests. NCHRP report 35, Transportation Research Board, Washington DC (1967)
Shahin, M.A., Jaksa, M.B., Maier, H.R.: Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36(1), 49–62 (2001)
Shahin, M.A., Maier, H.R., Jaksa, M.B.: Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering 18(2), 105–114 (2004)
Sharma, S., Das, A.: Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Canadian Journal of Civil Engineering 35(1), 57–66 (2008)
Skapura, D.M.: Building neural networks. ACM Press, New York (1996)
Sokal, R.R., Rohlf, F.J.: Biometry: The principles and practice of statistics in biological research. W.H. Freeman and Co., New York (1995)
Speckt, D.F.: A generalized regression neural network. IEEE transactions on neural networks 2(6), 568–576 (1991)
StatSoft Inc.: Electronic statistics textbook. Tulsa, Oklahoma (2006)
Tarefder, R.A., White, L., Zaman, M.: Neural network model for asphalt concrete permeability17(1), 19–27 (2005)
Thompson, M.R., Robnett, Q.L.: Resilient properties of subgrade soils. Final report, FHWA-IL-UI-160, University of Illinois, Urbana, Illinois (1976)
TRB. Use of artificial neural networks in geomechanical and pavement systems. Transportation research circular number E-C012, December 1999, Transportation Research Board, National Research Council, Washington DC (1999)
Uzan, J.: Characterization of granular material. Transportation Research Record 1022, 52–59 (1985)
Yau, A., Von Quintus, H.L.: Study of LTPP laboratory resilient modulus test data and response characteristics. Final report October 2002, FHWA-RD-02-051, USDOT, FHWA (2002)
Yildirim, T., Ozyilmaz, L.: Dimensionality reduction in conic section function neural network. Sadhana 27(6), 675–683 (2002)
Yoder, E.J., Witczak, M.W.: Principles of Pavement Design, 2nd edn. John Wiley & Son, Inc., New York (1975)
Zaman, M.M., Chen, D.H., Laguros, J.G.: Journal of Transportation Engineering 120(6), 967–988 (1994)
Zurada, J.M.: Introduction to artificial neural systems. West St. Paul, Minnesota (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Solanki, P., Zaman, M., Ebrahimi, A. (2009). Regression and Artificial Neural Network Modeling of Resilient Modulus of Subgrade Soils for Pavement Design Applications. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-04586-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04585-1
Online ISBN: 978-3-642-04586-8
eBook Packages: EngineeringEngineering (R0)