Investigation of geological, geotechnical and geophysical properties of Kiratli (Bayburt, NE Turkey) travertine

Predicting the Engineering Properties of Rocks from Textural Characteristics Using Some Soft Computing Approaches

“…Several researchers have attempted to develop various soft computing models for predicting different parameters from others in material sciences [16][17][18][19][20][21][22][23][24][25] and engineering properties of different rock types from their petrographic characteristics in engineering geology and rock mechanics [26][27][28][29][30][31][32][33][34][35]. In the recent years, some research works have been performed to assess correlations between mineralogical and textural characteristics and mechanical properties of different rocks by using statistical analyses and different soft computing approaches such as genetic programing (GP), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. The main advantages of these approaches are that (i) they have made it possible to solve nonlinear problems, in which mathematical models are not available, and (ii) they have introduced human knowledge such as cognition, recognition, understanding, learning, and others in the fields of computing [53].…”