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Survey of AI Methods for the Purpose of Geotechnical Profile Creation

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Advances in Soft and Hard Computing (ACS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 889))

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Abstract

The goal of this paper is to present methodology of unsupervised learning application in geotechnical data categorization. Geotechnical layers identification is conducted based on measurement from the Dilatometer of Marchetti Test taken at the campus of Warsaw University of Life Sciences. To cluster data, the Ant Clustering Algorithm, k-means, fuzzy sets and Self Organizing Map algorithms were introduced. All methods are adjusted to the presented problem and their efficiency compared. The paper is concluded with comments about the applications of computer intelligence methods for the geotechnical data analysis.

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References

  1. Barry, L.: Data Mining Techniques for Marketing. Sales and Customer Support. Wiley, New York (1996)

    Google Scholar 

  2. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  3. Bilski, P., Rabarijoely, S.: Automated soil categorization using the CPT and DMT investigations. In: Proceedings of the 2nd International Conference on New Developments in Soil Mechanics and Geotechnical Engineering ZM2009, Nicosia, Northern Cyprus, 28–30 May 2009, pp. 368–375 (2009)

    Google Scholar 

  4. Shahin, M.A., Jaksa, M.B., Maier, H.R.: Neural network based stochastic design charts for settlement prediction. Can. Geotech. J. 42, 110–120 (2005)

    Article  Google Scholar 

  5. Bilski, A.: Ant clustering for the CPT and DMT-based soil profile generation. In: IDAACS 2015 (2015)

    Google Scholar 

  6. Marchetti, S.: In situ tests by flat dilatometer. J. Geotech. GeoEng. Div. 106(GT3), 299–321 (1980). ASCE

    Google Scholar 

  7. Marchetti, S., Crapps, D.K.: Flat dilatometer manual. Internal report of GPE (1981)

    Google Scholar 

  8. Lechowicz Z., Rabarijoely, S.: Numerical analysis of organic soil behaviour during dilatometer test. Ann. Warsaw Agricult. Univ. Land Recl. 29, 41–49 (2000)

    Google Scholar 

  9. Tan, P.N, Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)

    Google Scholar 

  10. Davies, D.L., Bouldin D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224–227 (1979)

    Google Scholar 

  11. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)

    Article  Google Scholar 

  12. Deneubourg, J.-L., Goss, S., Franks, N., Sendora-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting. Robot-like ants and ant-like robots. In: Meyer, J.-A., Wilson, S. (eds.) Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)

    Google Scholar 

  13. Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 501–508. MIT Press, Cambridge (1994)

    Google Scholar 

  14. Kuntz, P., Snyers, D.: New results on an ant  =  based heuristic for highlighting the organization on of large graphs. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1451–1488. IEEE Press, Piscataway (1999)

    Google Scholar 

  15. Onan, A., Bukt, H., Korukoglu, S.: An improved ant algorithm with LDA-based representation for text document clustering. J. Inf. Sci. 43(2) (2017)

    Google Scholar 

  16. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999)

    Google Scholar 

  17. Tan, S.C., Ting, K.M., Teng, S.W.: Simplifying and improving ant-based clustering. Procedia Comput. Sci. 4, 46–55 (2011)

    Article  Google Scholar 

  18. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297. University of California Press (1967)

    Google Scholar 

  19. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)

    Article  Google Scholar 

  20. Bezdek, J.C.: Pattern Recognition With Fuzzy Objective Function Algorithms, p. 256. Plenum, New York (1981)

    Google Scholar 

  21. Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–37 (1973)

    Article  MathSciNet  Google Scholar 

  22. Kohonen, T.: Self-organizing Maps, vol. 30. Springer, Heidelberg (1995)

    Google Scholar 

  23. Murtagh, F.: Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognit. Lett. 16, 399–408 (1995)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Warsaw University of Life Sciences, Nowoursynowska 159, 02-776, Warsaw, Poland

    Adrian Bilski

Authors
  1. Adrian Bilski
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Correspondence to Adrian Bilski .

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Editors and Affiliations

  1. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Jerzy Pejaś

  2. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Imed El Fray

  3. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Tomasz Hyla

  4. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Bilski, A. (2019). Survey of AI Methods for the Purpose of Geotechnical Profile Creation. In: Pejaś, J., El Fray, I., Hyla, T., Kacprzyk, J. (eds) Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-030-03314-9_2

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