Prediction of rock mass rating system based on continuous functions using Chaos–ANFIS model

• Chaos–ANFIS model is the combination of chaos theory and ANFIS which investigate the uncertainties in the RMRb classification system based on continuous functions.
• Our proposed model proves the theory of Bieniawski that is based on non-linear parameters of RMRb system through strong reasoning and accurate algorithm based on mathematical concepts.
• It is observed that SCM prediction method has better results than the other proposed models for predicting RMRb classification system based on continuous functions with Chaos–ANFIS hybrid model.

Survey properties of soil and rock mass have always been associated with uncertainty. Hence, the behavior of the soil or rock cannot be investigated specifically by choosing a value specified for these properties. One of the most common systems for studying properties of rock mass is the rock mass classification system (RMR) which was developed by Bieniawski. In this system the input parameters are divided into several classes, and each class has particular rating. In this system, because of uncertainties of the input parameters, determining the definite boundary between the classes and assigning a specified value to a particular class is difficult, so when the input parameters are close to the boundary between the classes, the class rating with certainity is not decided. The aim of this paper is to propose a hybrid nonlinear Chaotic and Neuro-Fuzzy system modeling for the basic RMR system uncertainty based on continuous functions. This model also proves the theory of Bieniawski that is based on nonlinear systems by using chaos theory and mathematical relations. The main advantage of proposed model is to directly predict output of RMR system classification system without considering the input parameters so that it leads to better results and a higher level of prediction rock quality.

Figure optionsDownload as PowerPoint slide