[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Regression and Artificial Neural Network Modeling of Resilient Modulus of Subgrade Soils for Pavement Design Applications

  • Chapter
Intelligent and Soft Computing in Infrastructure Systems Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 259))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 103.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

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)

    Google Scholar 

  • 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)

    Google Scholar 

  • Bishop, C.: Neural networks for pattern recognition. University Press, Oxford (1995)

    Google Scholar 

  • Bors, A.G.: Introduction of the Radial Basis Function (RBF) Networks. Online Symp. for Electronics Engineers, DSP Algorithms: Multimedia 1(1), 1–7 (2001)

    Google Scholar 

  • 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)

    Google Scholar 

  • Dai, S., Zollars, J.: Resilient modulus of Minnesota road research project subgrade soil. Transportation Research Record 1786, 20–28 (2002)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • Fausett, L.V.: Fundamentals neural networks: Architecture, Algorithms and Applications. Prentice-Hall, Inc., Englewood Cliffs (1994)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • George, K.P.: Resilient Testing of soils using gyratory testing machine. Transportation Research Record 1369, 63–72 (1992)

    Google Scholar 

  • Gomes, A.C., Gillett, S.: Flexible pavement: Resilient behavior of soils. Balkema, Rotterdam (1996) ISBN 90 54 10 5232

    Google Scholar 

  • Haykin, W.L.: Neural networks: A comprehensive foundation. Macmillan College Publishing, New York (1994)

    MATH  Google Scholar 

  • Hill, T., Lewicki, P.: STATISTICS methods and applications. StatSoft, Tulsa, Oklahoma (2006)

    Google Scholar 

  • 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)

    Google Scholar 

  • Huang, Y.H.: Pavement analysis and design, 2nd edn. Prentice Hall, Upper Saddle River (2004)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer, Berlin (1989)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Li, D., Selig, E.T.: Resilient modulus for fine-grained subgrade soils. ASCE Journal of Geotechnical Engineering 120(6), 939–957 (1994)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • May, R.W., Witczak, M.W.: Effective granular modulus to model pavement response. Transportation Research Record 810, 1–9 (1981)

    Google Scholar 

  • Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of artificial neural networks. MIT Press, Cambridge (1996)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Moossazadeh, J.M., Witczak, M.W.: Prediction of subgrade moduli for soil that exhibits nonlinear behavior. Transportation Research Record 810, 9–17 (1981)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Narayan, S.: Using genetic algorithms to adapt neuron functional forms. In: Proc. Artificial Intelligence and Soft Computing, Banff, Canada (July 2002)

    Google Scholar 

  • NCHRP, Procedure for resilient modulus of unstabilized aggregate base and subgrade materials. Project 1-28A, Transportation Research Board, Washington D.C (2003)

    Google Scholar 

  • ODOT, Standards and specifications. Oklahoma Department of Transportation (2000)

    Google Scholar 

  • Patterson, D.: Artificial neural networks. Prentice Hall, Singapore (1996)

    MATH  Google Scholar 

  • Paute, J.L., Hornych, P.: Flexible pavement: Influence of water content on the cyclic behavior of a silty sand. Balkema, Rotterdam (1996)

    Google Scholar 

  • Raad, L., Minassian, G.H., Gartin, S.: Characterization of saturated granular bases under repeated loads. Transportation Research Record 1369, 73–82 (1992)

    Google Scholar 

  • Rada, C., Witczak, W.M.: Comprehensive evaluation of laboratory resilient moduli results for granular material. Transportation Research Record 810, 23–33 (1981)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • Ripley, B.D.: Pattern recognition and neural networks. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  • Rumelhart, D.E., McClelland, J.: Parallel distributed processing, vol. 1. MIT Press, Cambridge (1986)

    Google Scholar 

  • 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)

    Google Scholar 

  • Shahin, M.A., Jaksa, M.B., Maier, H.R.: Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36(1), 49–62 (2001)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Skapura, D.M.: Building neural networks. ACM Press, New York (1996)

    Google Scholar 

  • Sokal, R.R., Rohlf, F.J.: Biometry: The principles and practice of statistics in biological research. W.H. Freeman and Co., New York (1995)

    Google Scholar 

  • Speckt, D.F.: A generalized regression neural network. IEEE transactions on neural networks 2(6), 568–576 (1991)

    Article  Google Scholar 

  • StatSoft Inc.: Electronic statistics textbook. Tulsa, Oklahoma (2006)

    Google Scholar 

  • Tarefder, R.A., White, L., Zaman, M.: Neural network model for asphalt concrete permeability17(1), 19–27 (2005)

    Google Scholar 

  • Thompson, M.R., Robnett, Q.L.: Resilient properties of subgrade soils. Final report, FHWA-IL-UI-160, University of Illinois, Urbana, Illinois (1976)

    Google Scholar 

  • 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)

    Google Scholar 

  • Uzan, J.: Characterization of granular material. Transportation Research Record 1022, 52–59 (1985)

    Google Scholar 

  • 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)

    Google Scholar 

  • Yildirim, T., Ozyilmaz, L.: Dimensionality reduction in conic section function neural network. Sadhana 27(6), 675–683 (2002)

    Article  Google Scholar 

  • Yoder, E.J., Witczak, M.W.: Principles of Pavement Design, 2nd edn. John Wiley & Son, Inc., New York (1975)

    Google Scholar 

  • Zaman, M.M., Chen, D.H., Laguros, J.G.: Journal of Transportation Engineering 120(6), 967–988 (1994)

    Google Scholar 

  • Zurada, J.M.: Introduction to artificial neural systems. West St. Paul, Minnesota (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics