The Application of Genetic Algorithm for the Identification of Discontinuity Sets

불연속면 군 분류를 위한 유전자알고리즘의 응용

  • 선우춘 (한국지질자원연구원 지반안전연구부) ;
  • 정용복 (한국지질자원연구원 지반안전연구부)
  • Published : 2005.02.01

Abstract

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

Keywords

discontinuity set identification;GA(Genetic Algorithm);fitness;DBI;VI;orientation matrix

References

  1. Marcotte D. & Henry E., 2002, Automatic joint set clustering using mixture of bivariate normal distribution, Int. J Rock Mech. Min. Sci., 39, 323-334 https://doi.org/10.1016/S1365-1609(02)00033-3
  2. Markland, J., 1973, The analysis of principal components of orientation data, Int. J Rock Mech. Min. Sci., 11(3), 157-163
  3. Hammah, R. E. and Curran, J. H., 1998, Fuzzy cluster algorithm for the automatic identification of joint sets, Int. J Rock Mech. Min. Sci., 35(7), 889-905 https://doi.org/10.1016/S0148-9062(98)00011-4
  4. Holland J.H., 1975, Adaptation in natural and artificial system, Ann Arbor, The University of Michigan Press
  5. Treleaven, J. F., 1994, Genetic-algorithm programming environments, IEEE Computer 27(6):28-43
  6. 조영임, 1999, 최신 인공지능, 학문사, 364
  7. 정용복, 전석원, 2003, 절리군 분석을 위한 퍼지 클러스터링 기법, 터널과 지하공간, 13(4), 294-303
  8. Bandyopadhyay, S. and U. Maulik, 2002, Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition 35(6): 1197-1208 https://doi.org/10.1016/S0031-3203(01)00108-X