Automatic discontinuity set identification using genetic algorithm based clustering technique

The method of joint set identification using genetic algorithm was introduced. For handling of orientation data, the basic genetic algorithm was modified. We used real encoding scheme for the representation of candidate solutions and the orientation matrix for calculating mean direction of joint sets. The selection, crossover and mutation operations using real encoded chromosome were also implemented. Davies-Bouldin index and variance were used for cluster validity criteria. Finally, we developed GAC (Genetic Algorithm based Clustering), a FORTRAN program based on above algorithms and applied it to 3 different joint data sets. It is found that the results of joint set identification using GAC were acceptable for engineering design. From the application of GAC, we found that cluster validity index based on variance is more efficient in finding the number of clusters than Davis-Bouldin index. In addition, the genetic algorithm based clustering was proved to be a fast and efficient method for the joint set identification task.