Geomechanics and Engineering

  • 제1권1호
  • /
  • Pages.53-74
  • /
  • 2009
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Applications of artificial intelligence and data mining techniques in soil modeling

  • Javadi, A.A. (Computational Geomechanics Group, School of Engineering, Computing and Mathematics, University of Exeter) ;
  • Rezania, M. (Computational Geomechanics Group, School of Engineering, Computing and Mathematics, University of Exeter)
  • 투고 : 2008.10.21
  • 심사 : 2009.01.15
  • 발행 : 2009.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

In recent years, several computer-aided pattern recognition and data mining techniques have been developed for modeling of soil behavior. The main idea behind a pattern recognition system is that it learns adaptively from experience and is able to provide predictions for new cases. Artificial neural networks are the most widely used pattern recognition methods that have been utilized to model soil behavior. Recently, the authors have pioneered the application of genetic programming (GP) and evolutionary polynomial regression (EPR) techniques for modeling of soils and a number of other geotechnical applications. The paper reviews applications of pattern recognition and data mining systems in geotechnical engineering with particular reference to constitutive modeling of soils. It covers applications of artificial neural network, genetic programming and evolutionary programming approaches for soil modeling. It is suggested that these systems could be developed as efficient tools for modeling of soils and analysis of geotechnical engineering problems, especially for cases where the behavior is too complex and conventional models are unable to effectively describe various aspects of the behavior. It is also recognized that these techniques are complementary to conventional soil models rather than a substitute to them.

키워드

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