A Geotechnical KBS Using Fuzzy Logic

  • Peter W. Mullarkey
Conference paper


This paper presents an application of knowledge-based systems techniques involving a fuzzy logic framework in the domain of geotechnical engineering. Knowledge-based systems (KBS) are emerging as a powerful means of dealing with the ill-structured problems encountered in many engineering and medical applications. Stefik et al. [Stefik 82] state that expert systems are problem-solving programs that solve substantial problems generally conceded as being difficult and requiring expertise. They are called knowledge-based because their performance depends critically on the encoding of facts and heuristics (rules of thumb) used by experts. In a KBS the knowledge pertaining to the domain are encoded in the system in an explicit manner; this knowledge can be examined and modified, if necessary.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Barnett, J.A. Computational Methods for a Mathematical Theory of Evidence. Proceedings Seventh IJCAI, 1981, pp. 868–875.Google Scholar
  2. Begemann, H.K.S.Ph., Joustra, K., te Kamp, W.G.B., Krajicek, P.V.F.S, Heijnen, W.J. and van Weele, A.F. “Cone Penetration Testing”. Civiele and Bouwkundige Techniek, 3 (May 1982), 3–59.Google Scholar
  3. Buchanan, B.G. and Duda, R.O. Principles of Rule-Based Expert Systems. HPP-82–14, Stanford Univer¬sity, August, 1982. to appear in M. Yovits(ed.)Advances in Computers,Vol.22, New York, Academic Press.Google Scholar
  4. Chameau, J.L.A., Alteschaeffl, A., Michael, H.L. and Yao, J.T.P. “Potential Applications of Fuzzy Sets in Civil Engineering”. International Journal of Man-Machine Studies 19 (1983), 9–18.CrossRefGoogle Scholar
  5. De Ruiter, J. The Static Cone Penetrometer Test: State-of-the-Art-Report. Proceedings 2nd European Symposium on Penetration Testing, May, 1982, pp. 389–405.Google Scholar
  6. Douglas, B.J. and Olsen, R.S. Soil Classification Using the Electric Cone Penetrometer. In Cone Penetration Test Experience, Norris, G.M. and Holtz, R.D., Eds., ASCE, 1981, pp. 209–227.Google Scholar
  7. Staff. CPTData Processing. Earth Technology Corporation, 1983. Technical brochure.Google Scholar
  8. Ishizuka, M. “Inference Methods Based on Extended Dempster & Shafer’s Theory for Problems with Uncertain Fuzziness”. New Generation Computing 1 (1983). 159–168.CrossRefGoogle Scholar
  9. Mullarkey, P.W., Fenves, S.J. and Sangrey, D.A. CONE: An Expert System for Interpretation of Geotech-nical Characterization Data from Cone Penetrometers. R-85–147, Department of Civil Engineering, Carnegie-Mellon University, March, 1985.Google Scholar
  10. Olsen, R.S. Personal Correspondence.Google Scholar
  11. Rich, E. Artificial Intelligence. McGraw Hill, 1983.Google Scholar
  12. Schmucker, K.J. Fuzzy Sets, Natural Language Computations, and Risk Analysis. Computer Science Press, Inc., Rockville, Maryland, 1984.zbMATHGoogle Scholar
  13. Smith, R.G. and Young, R.L. The Design of the Dipmeter Advisor System. ACM Annual Conference, 1984, pp. 15–23.Google Scholar
  14. Stefik, M.J. et al. “The Organization of Expert Systems, A Tutorial”. Artificial Intelligence 18 (1982), 135–173.CrossRefGoogle Scholar
  15. Terzaghi, K. and Peck, R.B. Soil Mechanics in Engineering Practice. John Wiley and Sons, Inc., New York, 1967. 2nd Edition.Google Scholar
  16. Whalen, T. and Schott, B. “Issues in Fuzzy Production Systems”. International Journal of Man-Machine Studies 19(1983), 57–71. Georgia State University.CrossRefGoogle Scholar
  17. Zadeh, L.A. “Fuzzy Sets”. Information and Control 8 (June 1965), 338–353.CrossRefzbMATHMathSciNetGoogle Scholar
  18. Zadeh, L.A. “Making Computers Think Like People”. IEEE Spectrum (August 1984), 26–32.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Peter W. Mullarkey
    • 1
  1. 1.Schlumberger-Doll ResearchRidgefieldUSA

Personalised recommendations