Skip to main content
SpringerLink
Log in
Book cover

Computational Optimization and Applications in Engineering and Industry pp 221–243Cite as

Applications of Computational Intelligence in Behavior Simulation of Concrete Materials

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 359))

Abstract

The application of Computational Intelligence (CI) to structural engineering design problems is relatively new. This chapter presents the use of the CI techniques, and specifically Genetic Programming (GP) and Artificial Neural Network (ANN) techniques, in behavior modeling of concrete materials. We first introduce two main branches of GP, namely Tree-based Genetic Programming (TGP) and Linear Genetic Programming (LGP), and two variants of ANNs, called Multi Layer Perceptron (MLP) and Radial Basis Function (RBF). The simulation capabilities of these techniques are further demonstrated by applying them to two conventional concrete material cases. The first case is simulation of concrete compressive strength using mix properties and the second problem is prediction of elastic modulus of concrete using its compressive strength.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Walter, E., Pronzato, L.: Identification of parametric models from experimental data. Springer, London (1997)

    MATH  Google Scholar 

  2. Metenidis, M.F., Witczak, M., Korbicz, J.: A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem, Eng. Appl. Art. Int. 17, 363–370 (2004)

    Article  Google Scholar 

  3. Schwefel, H.P., Wegener, I., Weinert, K.: Advances in Computational Intelligence – Theory and Practice. Springer, Berlin (2002)

    Google Scholar 

  4. Sakla, S.S., Ashour, A.F.: Prediction of tensile capacity of single adhesive anchors using neural networks. Comput. Struct. 83(21–22), 1792–1803 (2005)

    Article  Google Scholar 

  5. Levasseur, S., Malécot, Y., Boulon, M., Flavigny, E.: Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data’. Int. J. Num. Anal. Meth. Geomech. 33(12), 1485–1511 (2009)

    Article  MATH  Google Scholar 

  6. Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  7. Johari, A., Habibagahi, G., Ghahramani, A.: Prediction of Soil-Water Characteristic Curve Using Genetic Programming. J. Geotech. Geoenvir. Eng. 132(5), 661–665 (2006)

    Article  Google Scholar 

  8. Brameier, M., Banzhaf, W.: Linear genetic programming. Springer Science + Business Media, New York (2007)

    MATH  Google Scholar 

  9. Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Tran. on Evol. Comput. 5(1), 17–26 (2001)

    Article  Google Scholar 

  10. Gandomi, A.H., Alavi, A.H., Mirzahosseini, M.R., Moqaddas Nejad, F.: Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures. J. Mater. Civil Eng. ASCE 23(3), 248–263 (2011)

    Google Scholar 

  11. Alavi, A.H., Gandomi, A.H.: A robust data mining approach for formulation of geotechnical engineering systems. Eng. Computations 28(3), 242–274 (2011)

    Article  Google Scholar 

  12. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, USA (1996)

    MATH  Google Scholar 

  13. Javadi, A.A., Rezania, M.: Applications of artificial intelligence and data mining techniques in soil modeling. Geomech. Eng. 1(1), 53–74 (2009)

    Google Scholar 

  14. Torres, R.S., Falcão, A.X., Gonçalves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A.: A genetic programming framework for content-based image retrieval. Pattern Recogn. 42(2), 283–292 (2009)

    Article  MATH  Google Scholar 

  15. Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic programming - an introduction. In: On the automatic evolution of computer programs and its application. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  16. Oltean, M., Grossan, C.: A comparison of several linear genetic programming techniques. Adv. Comp. Sys. 14(4), 1–29 (2003)

    Google Scholar 

  17. Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: Genetic programming: An introductory tutorial and a survey of techniques and applications. Technical report [CES–475], University of Essex, UK (2007)

    Google Scholar 

  18. Francone, F.D., Deschaine, L.M.: Extending the boundaries of design optimization by integrating fast optimization techniques with machine–code–based, linear genetic programming. Inf. Sci. 161, 99–120 (2004)

    Article  Google Scholar 

  19. Alavi, A.H., Gandomi, A.H., Heshmati, A.A.R.: Discussion on soft computing approach for real–time estimation of missing wave heights. Ocean Engineering 37, 1239–1240 (2010)

    Article  Google Scholar 

  20. Gandomi, A.H., Alavi, A.H., Sahab, M.G.: New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming. Mater. Struc. 43(7), 963–983 (2010)

    Article  Google Scholar 

  21. Perlovsky, L.I.: Neural networks and intellect. Oxford University Press, Oxford (2001)

    Google Scholar 

  22. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. MIT Press, Cambridge (1986)

    Google Scholar 

  23. Cybenko, J.: Approximations by Superpositions of a Sigmoidal Function. Math. Cont. Sign. Syst. 2, 303–314 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  24. Haykin, S.: Neural networks – A comprehensive foundation, 2nd edn. Prentice Hall Inc., Englewood Cliffs (1999)

    MATH  Google Scholar 

  25. Girosi, F., Poggio, T.: Networks and the best approximation property. Biological Cybernetics 63(3), 169–176 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  26. Gandomi, A.H., Alavi, A.H., Sahab, M.G., Arjmandi, P.: Formulation of Elastic Modulus of Concrete Using Linear Genetic Programming. Journal of Mechanical Science and Technology 24(6), 1011–1017 (2010)

    Article  Google Scholar 

  27. Silva, S.: GPLAB, a genetic programming toolbox for MATLAB (2007), http://gplab.sourceforge.net

  28. Conrads, M., Dolezal, O., Francone, F.D., Nordin, P.: Discipulus–fast genetic programming based on AIM learning technology. Register Machine Learning Technologies Inc., Littleton (2004)

    Google Scholar 

  29. MathWorks, Inc. MATLAB the language of technical computing, Version 7.4, Natick, MA, USA (2007)

    Google Scholar 

  30. Eberhart, R.C., Dobbins, R.W.: Neural Network PC Tools, A Practical Guide. Academic Press, San Diego (1990)

    Google Scholar 

  31. Popovics, S.: Analysis of the concrete strength versus water-cement ratio relationship. ACI Mater. J. 87(5), 517–529 (1990)

    Google Scholar 

  32. Oluokun, F.A.: Fly ash concrete mix design and the water–cement ratio law. ACI Mater. J. 91(4), 362–371 (1994)

    Google Scholar 

  33. Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cement Concrete Res. 28(12), 1797–1808 (1998)

    Article  Google Scholar 

  34. Yeh, I.C.: Exploring concrete slump model using artificial neural networks. J. Comput. Civil Eng. 20(3), 217–221 (2006)

    Article  Google Scholar 

  35. Yeh, I.C.: Analysis of strength of concrete using design of experiments and neural networks. ASCE, J. Mater. Civil Eng. 18(4), 597–604 (2006)

    Article  Google Scholar 

  36. Dunlop, P., Smith, S.: Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civil Eng. Environ. Syst. 20, 273–290 (2003)

    Article  Google Scholar 

  37. Khan, A., Cook, W.D., Mitchell, D.: Early age compressive stress– strain properties of low, medium and high strength concretes. ACI Mater. J. 92(6), 617–624 (1995)

    Google Scholar 

  38. McNaught, A.D., Wilkinson, A.: Compendium of Chemical Terminology. In: IUPAC, 2nd edn., Blackwell Scientific Publications, Oxford (1997)

    Google Scholar 

  39. Ferguson, P.M., Breen, J.E., Jirsa, J.O.: Reinforced Concrete Fundamentals, 5th edn. John Wiley & Sons, Chichester (1988)

    Google Scholar 

  40. ASTM-C469. Standard test method for static modulus of elasticity and poisson’s ratio of concrete in compression. Annual Book of ASTM standards (1994)

    Google Scholar 

  41. Demir, F.: A new way of prediction elastic modulus of normal and high strength concrete–fuzzy logic. Cem. Concr. Res. 35, 1531–1538 (2005)

    Article  Google Scholar 

  42. Demir, F.: Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Constr. Build. Mater. 22, 1428–1435 (2008)

    Article  Google Scholar 

  43. Smith, G.N.: Probability and statistics in civil engineering. Collins, London (1986)

    Google Scholar 

  44. Shahin, M.A., Jaksa, M.B., Maier, H.R.: Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Adv. Artif. Neur. Syst., Article ID 308239 (2009)

    Google Scholar 

  45. Kraslawski, A., Pedrycz, W., Nyström, L.: Fuzzy neural network as instance generator for case–based reasoning system: an example of selection of heat exchange equipment in mixing. Neural Computing and Applications 8(2), 106–113 (1999)

    Article  Google Scholar 

  46. Javadi, A.A., Tan, T.P., Elkassas, A.S.I.: Intelligent finite element method. In: the 3rd MIT Conference on Computational Fluid and Solid Mechanics, Cambridge, Massachusetts, USA (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Civil Engineering, Tafresh University, Tafresh, Iran

    Amir Hossein Gandomi

  2. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir Hossein Alavi

Authors
  1. Amir Hossein Gandomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Hossein Alavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Physical Laboratory, Mathematics and Scientific Computing, TW11 0LW, Teddington, Middlesex, UK

    Xin-She Yang

  2. Engineering Optimization & Modeling Center, School of Science and Engineering, Reykjavik University, Menntavegur 1, 101, Reykjavik, Iceland

    Slawomir Koziel

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Gandomi, A.H., Alavi, A.H. (2011). Applications of Computational Intelligence in Behavior Simulation of Concrete Materials. In: Yang, XS., Koziel, S. (eds) Computational Optimization and Applications in Engineering and Industry. Studies in Computational Intelligence, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20986-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20986-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20985-7

  • Online ISBN: 978-3-642-20986-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation