Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering

Abstract

Deformability of rock masses influencing their behavior is an important geomechanical property for the rock structures design. Due to the problems in determining the deformability of jointed rock masses at the laboratory-scale, various in situ test methods such as plate loading tests, dilatometer etc. have been developed. Although these methods are currently the best techniques, they are expensive and time consuming, and present operational problems. Furthermore, the influence of the test volume on modulus of deformation depending on the technique used is also important. For these reasons, in this paper, the adaptive network-based fuzzy inference system (ANFIS) was used to build a prediction model for the indirect estimation of deformation modulus of a rock mass. Three ANFIS models were implemented by grid partitioning (GP), subtractive clustering method (SCM) and fuzzy c-means clustering method (FCM). The estimation abilities offered using three ANFIS models were presented by using field data of achieved from road and railway construction sites in Korea. In these models, rock mass rating (RMR), depth, uniaxial compressive strength of intact rock (UCS) and elastic modulus of intact rock (Ei) were utilized as the input parameters, while the deformation modulus of a rock mass was the output parameter. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential to indirect estimation of deformation modulus of a rock mass with high degree of accuracy and robustness.